Publications

Additional (non-HLTCOE) publications may be found on researchers' personal websites.


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2014 (33 total)

Efficient Elicitation of Annotations for Human Evaluation of Machine Translation
Keisuke Sakaguchi, Matt Post and Benjamin Van Durme
Proceedings of the Workshop on Statistical Machine Translation – 2014

[bib]

@inproceedings{sakaguchi2014efficient, author = {Keisuke Sakaguchi and Post, Matt and Van Durme, Benjamin}, title = {Efficient Elicitation of Annotations for Human Evaluation of Machine Translation}, booktitle = {Proceedings of the Workshop on Statistical Machine Translation}, month = {June}, year = {2014}, address = {Baltimore, Maryland}, publisher = {Association for Computational Linguistics} }

Low-Resource Semantic Role Labeling
Matt Gormley, Margaret Mitchell, Benjamin Van Durme and Mark Dredze
Association for Computational Linguistics (ACL) – 2014

[bib]

@inproceedings{gormley-etal:2014:SRL, author = {Gormley, Matt and Mitchell, Margaret and Van Durme, Benjamin and Dredze, Mark}, title = {Low-Resource Semantic Role Labeling}, booktitle = {Association for Computational Linguistics (ACL)}, month = {June}, year = {2014}, url = {http://www.cs.jhu.edu/~mrg/publications/srl-acl-2014.pdf} }

Robust Feature Extraction Using Modulation Filtering of Autoregressive Models
Sriram Ganapathy, Sri Harish and Hynek Hermansky
2014

[abstract] [pdf] | [bib]

Abstract

Speaker and language recognition in noisy and degraded channel conditions continue to be a challenging problem mainly due to the mismatch between clean training and noisy test conditions. In the presence of noise, the most reliable portions of the signal are the high energy regions which can be used for robust feature extraction. In this paper, we propose a front end processing scheme based on autoregressive (AR) models that represent the high energy regions with good accuracy followed by a modulation filtering process. The AR model of the spectrogram is derived using two separable time and frequency AR transforms. The first AR model (temporal AR model) of the sub-band Hilbert envelopes is derived using frequency domain linear prediction (FDLP). This is followed by a spectral AR model applied on the FDLP envelopes. The output 2-D AR model represents a low-pass modulation filtered spectrogram of the speech signal. The band-pass modulation filtered spectrograms can further be derived by dividing two AR models with different model orders (cut-off frequencies). The modulation filtered spectrograms are converted to cepstral coefficients and are used for a speaker recognition task in noisy and reverberant conditions. Various speaker recognition experiments are performed with clean and noisy versions of the NIST-2010 speaker recognition evaluation (SRE) database using the state-of-the-art speaker recognition system. In these experiments, the proposed front-end analysis provides substantial improvements (relative improvements of up to 25%) compared to baseline techniques. Furthermore, we also illustrate the generalizability of the proposed methods using language identification (LID) experiments on highly degraded high-frequency (HF) radio channels and speech recognition experiments on noisy data.
@{, author = {Ganapathy, Sriram and Sri Harish and Hynek Hermansky}, title = {Robust Feature Extraction Using Modulation Filtering of Autoregressive Models}, month = {June}, year = {2014}, publisher = {IEEE}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6826560&queryText%3DRobust+Feature+Extraction+Using+Modulation+Filtering+of+Autoregressive+models}, abstract = {Speaker and language recognition in noisy and degraded channel conditions continue to be a challenging problem mainly due to the mismatch between clean training and noisy test conditions. In the presence of noise, the most reliable portions of the signal are the high energy regions which can be used for robust feature extraction. In this paper, we propose a front end processing scheme based on autoregressive (AR) models that represent the high energy regions with good accuracy followed by a modulation filtering process. The AR model of the spectrogram is derived using two separable time and frequency AR transforms. The first AR model (temporal AR model) of the sub-band Hilbert envelopes is derived using frequency domain linear prediction (FDLP). This is followed by a spectral AR model applied on the FDLP envelopes. The output 2-D AR model represents a low-pass modulation filtered spectrogram of the speech signal. The band-pass modulation filtered spectrograms can further be derived by dividing two AR models with different model orders (cut-off frequencies). The modulation filtered spectrograms are converted to cepstral coefficients and are used for a speaker recognition task in noisy and reverberant conditions. Various speaker recognition experiments are performed with clean and noisy versions of the NIST-2010 speaker recognition evaluation (SRE) database using the state-of-the-art speaker recognition system. In these experiments, the proposed front-end analysis provides substantial improvements (relative improvements of up to 25%) compared to baseline techniques. Furthermore, we also illustrate the generalizability of the proposed methods using language identification (LID) experiments on highly degraded high-frequency (HF) radio channels and speech recognition experiments on noisy data.} }

Some Insights From Translating Conversational Telephone Speech
Gaurav Kumar, Matt Post, Daniel Povey and Sanjeev Khudanpur
International Conference on Acoustics, Speech, and Signal Processing (ICASSP) – 2014

[bib]

@inproceedings{kumar2014some, author = {Gaurav Kumar and Post, Matt and Povey, Daniel and Khudanpur, Sanjeev}, title = {Some Insights From Translating Conversational Telephone Speech}, booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, month = {May}, year = {2014}, address = {Florence, Italy}, url = {http://cs.jhu.edu/~post/papers/kumar2013some.pdf} }

Population Health Concerns During the United States' Great Recession
Ben Althouse, Jon-Patrick Allem, Matt Childers, Mark Dredze and John Ayers
American Journal of Preventive Medicine – 2014

[bib]

@article{Althouse:2014lr, author = {Ben Althouse and Jon-Patrick Allem and Matt Childers and Dredze, Mark and John Ayers}, title = {Population Health Concerns During the United States' Great Recession}, month = {February}, year = {2014}, pages = {166-170} }

The Language Demographics of Amazon Mechanical Turk
Ellie Pavlick, Matt Post, Ann Irvine, Dmitry Kachaev and Chris Callison-Burch
Transactions of the Association for Computational Linguistics – 2014

[bib]

@article{pavlick2014language, author = {Ellie Pavlick and Post, Matt and Irvine, Ann and Dmitry Kachaev and Callison-Burch, Chris}, title = {The Language Demographics of Amazon Mechanical Turk}, month = {February}, year = {2014}, pages = {79--92}, url = {http://www.cis.upenn.edu/~ccb/publications/language-demographics-of-mechanical-turk.pdf} }

Reducing Reliance on Relevance Judgments for System Comparison by Using Expectation-Maximization
Ning Gao, William Webber and Douglas W Oard
The 36th European Conference on Information Retrieval – 2014

[bib]

@inproceedings{Gao2014ECIR, author = {Ning Gao and William Webber and Douglas W Oard}, title = {Reducing Reliance on Relevance Judgments for System Comparison by Using Expectation-Maximization}, booktitle = {The 36th European Conference on Information Retrieval}, year = {2014}, publisher = {Springer}, pages = {1--12}, url = {http://terpconnect.umd.edu/~oard/pdf/ecir14.pdf} }

A Wikipedia-based Corpus for Contextualized Machine Translation.
Jennifer Drexler, Pushpendre Rastogi, Jacqueline Aguilar, Benjamin Van Durme and Matt Post
Proceedings of the Eighth international Conference on Language Resources and Evaluation (LREC) – 2014

[bib]

@inproceedings{Drexler2014, author = {Jennifer Drexler and Pushpendre Rastogi and Aguilar, Jacqueline and Van Durme, Benjamin and Post, Matt}, title = {A Wikipedia-based Corpus for Contextualized Machine Translation.}, booktitle = {Proceedings of the Eighth international Conference on Language Resources and Evaluation (LREC)}, year = {2014} }

Learning Polylingual Topic Models from Code-Switched Social Media Documents
Nanyun Peng, Yiming Wang and Mark Dredze
Association for Computational Linguistics (ACL) – 2014

[abstract] [bib]

Abstract

Code-switched documents are common in social media, providing evidence for polylingual topic models to infer aligned topics across languages. We present Code-Switched LDA (csLDA), which infers language specific topic distributions based on code-switched documents to facilitate multi-lingual corpus analysis. We experiment on two code-switching corpora (English-Spanish Twitter data and English-Chinese Weibo data) and show that csLDA improves perplexity over LDA, and learns semantically coherent aligned topics as judged by human annotators.
@inproceedings{Peng:2014fk, author = {Nanyun Peng and Yiming Wang and Dredze, Mark}, title = {Learning Polylingual Topic Models from Code-Switched Social Media Documents}, booktitle = {Association for Computational Linguistics (ACL)}, year = {2014}, abstract = {Code-switched documents are common in social media, providing evidence for polylingual topic models to infer aligned topics across languages. We present Code-Switched LDA (csLDA), which infers language specific topic distributions based on code-switched documents to facilitate multi-lingual corpus analysis. We experiment on two code-switching corpora (English-Spanish Twitter data and English-Chinese Weibo data) and show that csLDA improves perplexity over LDA, and learns semantically coherent aligned topics as judged by human annotators.} }

Measuring Post Traumatic Stress Disorder in Twitter
Glen Coppersmith, Craig Harman and Mark Dredze
International Conference on Weblogs and Social Media (ICWSM) – 2014

[abstract] [bib]

Abstract

Traditional mental health studies rely on information primarily collected and analyzed through personal contact with a health care professional. Recent work has shown the utility of social media data for studying depression, but there have been limited evaluations of other mental health conditions. We consider post traumatic stress disorder (PTSD), a serious condition that affects millions worldwide, with especially high rates in military veterans. We show how to obtain a PTSD classifier for social media using simple searches of available Twitter data, a significant reduction in training data cost compared to previous work on mental health. We demonstrate its utility by an examination of language use from PTSD individuals, and by detecting elevated rates of PTSD at and around US military bases using our classifiers.
@inproceedings{Coppersmith:2014lr, author = {Coppersmith, Glen and Harman, Craig and Dredze, Mark}, title = {Measuring Post Traumatic Stress Disorder in Twitter}, booktitle = {International Conference on Weblogs and Social Media (ICWSM)}, year = {2014}, abstract = {Traditional mental health studies rely on information primarily collected and analyzed through personal contact with a health care professional. Recent work has shown the utility of social media data for studying depression, but there have been limited evaluations of other mental health conditions. We consider post traumatic stress disorder (PTSD), a serious condition that affects millions worldwide, with especially high rates in military veterans. We show how to obtain a PTSD classifier for social media using simple searches of available Twitter data, a significant reduction in training data cost compared to previous work on mental health. We demonstrate its utility by an examination of language use from PTSD individuals, and by detecting elevated rates of PTSD at and around US military bases using our classifiers.} }

Robust Entity Clustering via Phylogenetic Inference
Nicholas Andrews, Jason Eisner and Mark Dredze
Association for Computational Linguistics (ACL) – 2014

[abstract] [bib]

Abstract

Entity clustering must determine when two named-entity mentions refer to the same entity. Typical approaches use a pipeline architecture that clusters the mentions using fixed or learned measures of name and context similarity. In this paper, we propose a model for cross-document coreference resolution that achieves robustness by learning similarity from unlabeled data. The generative process assumes that each entity mention arises from copying and optionally mutating an earlier name from a similar context. Clustering the mentions into entities depends on recovering this copying tree jointly with estimating models of the mutation process and parent selection process. We present a block Gibbs sampler for posterior inference and an empirical evalution on several datasets. On a challenging Twitter corpus, our method outperforms the best baseline by 12.6 points of F1 score.
@inproceedings{Andrews:2014fk, author = {Andrews, Nicholas and Eisner, Jason and Dredze, Mark}, title = {Robust Entity Clustering via Phylogenetic Inference}, booktitle = {Association for Computational Linguistics (ACL)}, year = {2014}, abstract = {Entity clustering must determine when two named-entity mentions refer to the same entity. Typical approaches use a pipeline architecture that clusters the mentions using fixed or learned measures of name and context similarity. In this paper, we propose a model for cross-document coreference resolution that achieves robustness by learning similarity from unlabeled data. The generative process assumes that each entity mention arises from copying and optionally mutating an earlier name from a similar context. Clustering the mentions into entities depends on recovering this copying tree jointly with estimating models of the mutation process and parent selection process. We present a block Gibbs sampler for posterior inference and an empirical evalution on several datasets. On a challenging Twitter corpus, our method outperforms the best baseline by 12.6 points of F1 score.} }

Quantifying Mental Health Signals in Twitter
Glen Coppersmith, Mark Dredze and Craig Harman
ACL Workshop on Computational Linguistics and Clinical Psychology – 2014

[abstract] [bib]

Abstract

The ubiquity of social media provides a rich opportunity to enhance the data available to mental health clinicians and researchers, enabling a better-informed and better-equipped mental health field. We present analysis of mental health phenomena in publicly available Twitter data, demonstrating how rigorous application of simple natural language processing methods can yield insight into specific disorders as well as mental health writ large, along with evidence that as-of-yet undiscovered linguistic signals relevant to mental health exist in social media. We present a novel method for gathering data for a range of mental illnesses quickly and cheaply, then focus on analysis of four in particular: post-traumatic stress disorder (PTSD), major depressive disorder, bipolar disorder, and seasonal affective disorder. We intend for these proof-of-concept results to inform the necessary ethical discussion regarding the balance between the utility of such data and the privacy of mental health related information.
@inproceedings{Coppersmith:2014fk, author = {Coppersmith, Glen and Dredze, Mark and Harman, Craig}, title = {Quantifying Mental Health Signals in Twitter}, booktitle = {ACL Workshop on Computational Linguistics and Clinical Psychology}, year = {2014}, abstract = {The ubiquity of social media provides a rich opportunity to enhance the data available to mental health clinicians and researchers, enabling a better-informed and better-equipped mental health field. We present analysis of mental health phenomena in publicly available Twitter data, demonstrating how rigorous application of simple natural language processing methods can yield insight into specific disorders as well as mental health writ large, along with evidence that as-of-yet undiscovered linguistic signals relevant to mental health exist in social media. We present a novel method for gathering data for a range of mental illnesses quickly and cheaply, then focus on analysis of four in particular: post-traumatic stress disorder (PTSD), major depressive disorder, bipolar disorder, and seasonal affective disorder. We intend for these proof-of-concept results to inform the necessary ethical discussion regarding the balance between the utility of such data and the privacy of mental health related information.} }

A Comparison of the Events and Relations Across ACE, ERE, TAC-KBP, and FrameNet Annotation Standards.
Jacqueline Aguilar, Charley Beller, Paul McNamee, Benjamin Van Durme, Stephanie Strassel, Zhiyi Song and Joe Ellis
ACL Workshop: EVENTS – 2014

[bib]

@inproceedings{Aguilar2014, author = {Aguilar, Jacqueline and Charley Beller and McNamee, Paul and Van Durme, Benjamin and Stephanie Strassel and Zhiyi Song and Joe Ellis}, title = {A Comparison of the Events and Relations Across ACE, ERE, TAC-KBP, and FrameNet Annotation Standards.}, booktitle = {ACL Workshop: EVENTS}, year = {2014} }

Facebook, Twitter and Google Plus for Breaking News: Is there a winner?
Miles Osborne and Mark Dredze
International Conference on Weblogs and Social Media (ICWSM) – 2014

[abstract] [bib]

Abstract

Twitter is widely seen as being the go to place for breaking news. Recently however, competing Social Media have begun to carry news. Here we examine how Facebook, Google Plus and Twitter report on breaking news. We consider coverage (whether news events are reported) and latency (the time when they are reported). Using data drawn from three weeks in December 2013, we identify 29 major news events, ranging from celebrity deaths, plague outbreaks to sports events. We find that all media carry the same major events, but Twitter continues to be the preferred medium for breaking news, almost consistently leading Facebook or Google Plus. Facebook and Google Plus largely repost newswire stories and their main research value is that they conveniently package multitple sources of information together.
@inproceedings{Osborne:2014fk, author = {Miles Osborne and Dredze, Mark}, title = {Facebook, Twitter and Google Plus for Breaking News: Is there a winner?}, booktitle = {International Conference on Weblogs and Social Media (ICWSM)}, year = {2014}, abstract = {Twitter is widely seen as being the go to place for breaking news. Recently however, competing Social Media have begun to carry news. Here we examine how Facebook, Google Plus and Twitter report on breaking news. We consider coverage (whether news events are reported) and latency (the time when they are reported). Using data drawn from three weeks in December 2013, we identify 29 major news events, ranging from celebrity deaths, plague outbreaks to sports events. We find that all media carry the same major events, but Twitter continues to be the preferred medium for breaking news, almost consistently leading Facebook or Google Plus. Facebook and Google Plus largely repost newswire stories and their main research value is that they conveniently package multitple sources of information together.} }

Featherweight Phonetic Keyword Search for Conversational Speech
Keith Kintzley, Aren Jansen and Hynek Hermansky
International Conference on Acoustics, Speech, and Signal Processing (ICASSP) – 2014

[bib]

@inproceedings{kintzleyfeatherweight, author = {Keith Kintzley and Jansen, Aren and Hermansky, Hynek}, title = {Featherweight Phonetic Keyword Search for Conversational Speech}, booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {2014} }

Unsupervised Idiolect Discovery for Speaker Recognition
Aren Jansen, Daniel Garcia-Romero, Pascal Clark and Jaime Hernandez-Cordero
International Conference on Acoustics, Speech, and Signal Processing (ICASSP) – 2014

[bib]

@inproceedings{jansenidiolect, author = {Jansen, Aren and Garcia-Romero, Daniel and Clark, Pascal and Jaime Hernandez-Cordero}, title = {Unsupervised Idiolect Discovery for Speaker Recognition}, booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {2014} }

Bridging the Gap between Speech Technology and Natural Language Processing: An Evaluation Toolbox for Term Discovery Systems
Bogdan Ludusan, Maarten Versteegh, Aren Jansen, Guillaume Gravier, Xuan-Nga Cao, Mark Johnson and Emmanuel Dupoux
Proceedings of the Eighth international Conference on Language Resources and Evaluation (LREC) – 2014

[bib]

@inproceedings{jansenlrec, author = {Bogdan Ludusan and Maarten Versteegh and Jansen, Aren and Guillaume Gravier and Xuan-Nga Cao and Mark Johnson and Emmanuel Dupoux}, title = {Bridging the Gap between Speech Technology and Natural Language Processing: An Evaluation Toolbox for Term Discovery Systems}, booktitle = {Proceedings of the Eighth international Conference on Language Resources and Evaluation (LREC)}, year = {2014} }

Could Behavioral Medicine Lead the Web Data Revolution?
John Ayers, Benjamin Althouse and Mark Dredze
Journal of the American Medical Association (JAMA) – 2014

[bib]

@article{Ayers:2014fk, author = {John Ayers and Benjamin Althouse and Dredze, Mark}, title = {Could Behavioral Medicine Lead the Web Data Revolution?}, year = {2014} }

Improving Lexical Embeddings with Semantic Knowledge
Mo Yu and Mark Dredze
Association for Computational Linguistics (ACL) – 2014

[abstract] [bib]

Abstract

Word embeddings learned on unlabeled data are a popular tool in semantics, but may not capture the desired semantics. We propose a new learning objective that incorporates both a neural language model objective and prior knowledge from semantic resources to learn improved lexical semantic embeddings. We demonstrate that our embeddings improve over those learned solely on raw text in three settings: language modeling, measuring semantic similarity, and predicting human judgements.
@inproceedings{Yu:2014, author = {Mo Yu and Dredze, Mark}, title = {Improving Lexical Embeddings with Semantic Knowledge}, booktitle = {Association for Computational Linguistics (ACL)}, year = {2014}, abstract = {Word embeddings learned on unlabeled data are a popular tool in semantics, but may not capture the desired semantics. We propose a new learning objective that incorporates both a neural language model objective and prior knowledge from semantic resources to learn improved lexical semantic embeddings. We demonstrate that our embeddings improve over those learned solely on raw text in three settings: language modeling, measuring semantic similarity, and predicting human judgements.} }

What's the Healthiest Day? Circaseptan (Weekly) Rhythms in Healthy Considerations
John Ayers, Benjamin Althouse, Morgan Johnson, Mark Dredze and Joanna Cohen
American Journal of Preventive Medicine – 2014

[bib]

@article{Ayers:2014lr, author = {John Ayers and Benjamin Althouse and Morgan Johnson and Dredze, Mark and Joanna Cohen}, title = {What's the Healthiest Day? Circaseptan (Weekly) Rhythms in Healthy Considerations}, year = {2014} }

Biases in Predicting the Human Language Model
Alex Fine, Austin Frank, T. Jaeger and Benjamin Van Durme
Association for Computational Linguistics (ACL), Short Papers – 2014

[bib]

@inproceedings{FineFrankJaegerVanDurmeACL14, author = {Alex Fine and Austin Frank and T. Jaeger and Van Durme, Benjamin}, title = {Biases in Predicting the Human Language Model}, booktitle = {Association for Computational Linguistics (ACL), Short Papers}, year = {2014} }

I'm a Belieber: Social Roles via Self-identification and Conceptual Attributes
Charley Beller, Rebecca Knowles, Craig Harman, Shane Bergsma, Margaret Mitchell and Benjamin Van Durme
Association for Computational Linguistics (ACL), Short Papers – 2014

[bib]

@inproceedings{BellerKnowlesHarmanBergsmaMitchellVanDurmeACL14, author = {Charley Beller and Rebecca Knowles and Harman, Craig and Bergsma, Shane and Mitchell, Margaret and Van Durme, Benjamin}, title = {I'm a Belieber: Social Roles via Self-identification and Conceptual Attributes}, booktitle = {Association for Computational Linguistics (ACL), Short Papers}, year = {2014} }

Freebase QA: Information Extraction or Semantic Parsing?
Xuchen Yao, Jonathan Berant and Benjamin Van Durme
Association for Computational Linguistics (ACL), Workshop on Semantic Parsing – 2014

[bib]

@inproceedings{YaoBerantVanDurmeACL14, author = {Xuchen Yao and Jonathan Berant and Van Durme, Benjamin}, title = {Freebase QA: Information Extraction or Semantic Parsing?}, booktitle = {Association for Computational Linguistics (ACL), Workshop on Semantic Parsing}, year = {2014} }

A Comparison of the Events and Relations Across ACE, ERE, TAC-KBP, and FrameNet Annotation Standards
Jacqueline Aguilar, Charley Beller, Paul McNamee, Benjamin Van Durme, Stephanie Strassel, Zhiyi Song and Joe Ellis
Association for Computational Linguistics (ACL), Workshop on EVENTS – 2014

[bib]

@inproceedings{AguilarBellerMcNameeVanDurmeStrasselSongEllisACL14, author = {Aguilar, Jacqueline and Charley Beller and McNamee, Paul and Van Durme, Benjamin and Stephanie Strassel and Zhiyi Song and Joe Ellis}, title = {A Comparison of the Events and Relations Across ACE, ERE, TAC-KBP, and FrameNet Annotation Standards}, booktitle = {Association for Computational Linguistics (ACL), Workshop on EVENTS}, year = {2014} }

Is the Stanford Dependency Representation Semantic?
Rachel Rudinger and Benjamin Van Durme
Association for Computational Linguistics (ACL), Workshop on EVENTS – 2014

[bib]

@inproceedings{RudingerVanDurmeACL14, author = {Rachel Rudinger and Van Durme, Benjamin}, title = {Is the Stanford Dependency Representation Semantic?}, booktitle = {Association for Computational Linguistics (ACL), Workshop on EVENTS}, year = {2014} }

Augmenting FrameNet Via PPDB
Pushpendre Rastogi and Benjamin Van Durme
Association for Computational Linguistics (ACL), Workshop on EVENTS – 2014

[bib]

@inproceedings{RastogiVanDurmeACL14, author = {Pushpendre Rastogi and Van Durme, Benjamin}, title = {Augmenting FrameNet Via PPDB}, booktitle = {Association for Computational Linguistics (ACL), Workshop on EVENTS}, year = {2014} }

Predicting Fine-grained Social Roles with Selectional Preferences
Charley Beller, Craig Harman and Benjamin Van Durme
Association for Computational Linguistics (ACL), Workshop on Language Technologies and Computational Social Science (LACSS) – 2014

[bib]

@inproceedings{BellerHarmanVanDurmeACL14, author = {Charley Beller and Harman, Craig and Van Durme, Benjamin}, title = {Predicting Fine-grained Social Roles with Selectional Preferences}, booktitle = {Association for Computational Linguistics (ACL), Workshop on Language Technologies and Computational Social Science (LACSS)}, year = {2014} }

A Wikipedia-based Corpus for Contextualized Machine Translation
Jennifer Drexler, Pushpendre Rastogi, Jacqueline Aguilar, Benjamin Van Durme and Matt Post
Proceedings of the Eighth international Conference on Language Resources and Evaluation (LREC) – 2014

[bib]

@inproceedings{DrexlerRastogiAguilarVanDurmePostACL14, author = {Jennifer Drexler and Pushpendre Rastogi and Aguilar, Jacqueline and Van Durme, Benjamin and Post, Matt}, title = {A Wikipedia-based Corpus for Contextualized Machine Translation}, booktitle = {Proceedings of the Eighth international Conference on Language Resources and Evaluation (LREC)}, year = {2014} }

Information Extraction over Structured Data: Question Answering with Freebase
Xuchen Yao and Benjamin Van Durme
Association for Computational Linguistics (ACL) – 2014

[bib]

@inproceedings{YaoVanDurmeACL14, author = {Xuchen Yao and Van Durme, Benjamin}, title = {Information Extraction over Structured Data: Question Answering with Freebase}, booktitle = {Association for Computational Linguistics (ACL)}, year = {2014} }

Inferring User Political Preferences from Streaming Communications
Svitlana Volkova, Glen Coppersmith and Benjamin Van Durme
Association for Computational Linguistics (ACL) – 2014

[bib]

@inproceedings{VolkovaCoppersmithVanDurmeACL14, author = {Volkova, Svitlana and Coppersmith, Glen and Van Durme, Benjamin}, title = {Inferring User Political Preferences from Streaming Communications}, booktitle = {Association for Computational Linguistics (ACL)}, year = {2014} }

Particle Filter Rejuvenation and Latent Dirichlet Allocation
Chandler May, Alex Clemmer and Benjamin Van Durme
Association for Computational Linguistics (ACL), Short Papers – 2014

[bib]

@inproceedings{MayClemmerVanDurmeACL14, author = {Chandler May and Alex Clemmer and Van Durme, Benjamin}, title = {Particle Filter Rejuvenation and Latent Dirichlet Allocation}, booktitle = {Association for Computational Linguistics (ACL), Short Papers}, year = {2014} }

Exponential Reservoir Sampling for Streaming Language Models
Miles Osborne, Ashwin Lall and Benjamin Van Durme
Association for Computational Linguistics (ACL), Short Papers – 2014

[bib]

@inproceedings{OsborneLallVanDurmeACL14, author = {Miles Osborne and Ashwin Lall and Van Durme, Benjamin}, title = {Exponential Reservoir Sampling for Streaming Language Models}, booktitle = {Association for Computational Linguistics (ACL), Short Papers}, year = {2014} }

A long, deep and wide artificial neural net for robust speech recognition in unknown noise
Feipeng Li, Phani Sankar Nidadavolu and Hynek Hermansky
2014

[abstract] [bib]

Abstract

A long deep and wide artificial neural net (LDWNN) with multiple ensemble neural nets for individual frequency subbands is proposed for robust speech recognition in unknown noise. It is assumed that the effect of arbitrary additive noise on speech recognition can be approximated by white noise (or speech-shaped noise) of similar level across multiple frequency subbands. The ensemble neural nets are trained in clean and speech-shaped noise at 20, 10, and 5 dB SNR to accommodate noise of different levels, followed by a neural net trained to select the most suitable neural net for optimum information extraction within a frequency subband. The posteriors from multiple frequency subbands are fused by another neural net to give a more reliable estimation. Experimental results show that the subband ensemble net adapts well to unknown noise.
@{, author = {Feipeng Li and Phani Sankar Nidadavolu and Hermansky, Hynek}, title = {A long, deep and wide artificial neural net for robust speech recognition in unknown noise}, year = {2014}, publisher = {INTERSPEECH}, url = {http://www.researchgate.net/publication/261707505_A_long_deep_and_wide_artificial_neural_net_for_robust_speech_recognition_in_unknown_noise}, abstract = {A long deep and wide artificial neural net (LDWNN) with multiple ensemble neural nets for individual frequency subbands is proposed for robust speech recognition in unknown noise. It is assumed that the effect of arbitrary additive noise on speech recognition can be approximated by white noise (or speech-shaped noise) of similar level across multiple frequency subbands. The ensemble neural nets are trained in clean and speech-shaped noise at 20, 10, and 5 dB SNR to accommodate noise of different levels, followed by a neural net trained to select the most suitable neural net for optimum information extraction within a frequency subband. The posteriors from multiple frequency subbands are fused by another neural net to give a more reliable estimation. Experimental results show that the subband ensemble net adapts well to unknown noise.} }

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2013 (68 total)

Perceptual Properties of Current Speech Recognition Technology
Hynek Hermansky, Jordan R. Cohen and Richard M. Stern
2013

[abstract] [pdf] | [bib]

Abstract

In recent years, a number of feature extraction procedures for automatic speech recognition (ASR) systems have been based on models of human auditory processing, and one often hears arguments in favor of implementing knowledge of human auditory perception and cognition into machines for ASR. This paper takes a reverse route, and argues that the engineering techniques for automatic recognition of speech that are already in widespread use are often consistent with some well-known properties of the human auditory system.
@{, author = {Hermansky, Hynek and Jordan R. Cohen and Richard M. Stern}, title = {Perceptual Properties of Current Speech Recognition Technology}, month = {September}, year = {2013}, publisher = {IEEE}, pages = {1968 - 1985}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6566018}, abstract = {In recent years, a number of feature extraction procedures for automatic speech recognition (ASR) systems have been based on models of human auditory processing, and one often hears arguments in favor of implementing knowledge of human auditory perception and cognition into machines for ASR. This paper takes a reverse route, and argues that the engineering techniques for automatic recognition of speech that are already in widespread use are often consistent with some well-known properties of the human auditory system.} }

Findings of the 2013 Workshop on Statistical Machine Translation
Ondrej Bojar, Christian Buck, Chris Callison-Burch, Christian Federmann, Barry Haddow, Philipp Koehn, Christof Monz, Matt Post, Radu Soricut and Lucia Specia
Eighth Workshop on Statistical Machine Translation – 2013

[bib]

@inproceedings{bojar-EtAl:2013:WMT, author = {Ondrej Bojar and Christian Buck and Callison-Burch, Chris and Christian Federmann and Barry Haddow and Philipp Koehn and Christof Monz and Post, Matt and Radu Soricut and Lucia Specia}, title = {Findings of the 2013 Workshop on Statistical Machine Translation}, booktitle = {Eighth Workshop on Statistical Machine Translation}, month = {August}, year = {2013}, address = {Sofia, Bulgaria}, publisher = {Association for Computational Linguistics}, pages = {1--44}, url = {http://www.aclweb.org/anthology/W13-2201} }

Joshua 5.0: Sparser, Better, Faster, Server
Matt Post, Juri Ganitkevitch, , Jonathan Weese, Yuan Cao and Chris Callison-Burch
Eighth Workshop on Statistical Machine Translation – 2013

[bib]

@inproceedings{post-EtAl:2013:WMT, author = {Post, Matt and Juri Ganitkevitch and and Jonathan Weese and Yuan Cao and Callison-Burch, Chris}, title = {Joshua 5.0: Sparser, Better, Faster, Server}, booktitle = {Eighth Workshop on Statistical Machine Translation}, month = {August}, year = {2013}, address = {Sofia, Bulgaria}, publisher = {Association for Computational Linguistics}, pages = {206--212}, url = {http://www.aclweb.org/anthology/W13-2226} }

Combining Bilingual and Comparable Corpora for Low Resource Machine Translation
Ann Irvine and Chris Callison-Burch
Eighth Workshop on Statistical Machine Translation – 2013

[bib]

@inproceedings{irvine-callisonburch:2013:WMT, author = {Irvine, Ann and Callison-Burch, Chris}, title = {Combining Bilingual and Comparable Corpora for Low Resource Machine Translation}, booktitle = {Eighth Workshop on Statistical Machine Translation}, month = {August}, year = {2013}, address = {Sofia, Bulgaria}, publisher = {Association for Computational Linguistics}, pages = {262--270}, url = {http://www.aclweb.org/anthology/W13-2233} }

Multi-stream recognition of noisy speech with performance monitoring
Ehsan Variani, Feipeng Li and Hynek Hermansky
2013

[pdf] | [bib]

@{, author = {Ehsan Variani and Feipeng Li and Hermansky, Hynek}, title = {Multi-stream recognition of noisy speech with performance monitoring}, month = {August}, year = {2013}, pages = {2978--2981}, url = {http://www.isca-speech.org/archive/interspeech_2013/i13_2978.html} }

Developing a Speaker Identification System for The Darpa Rats Project
Oldrich Plchot, Spyros Matsoukas, Pavel Matejka, Najim Dehak, Jeff Ma, S. Cumani, O. Glembek, Hynek Hermansky, S.H. Mallidi, N. Mesgarani, R. Schwartz, M. Soufifar, Z.H. Tan, S. Thomas, B. Zhang and X. Zhou
Proceedings of ICASSP 2013 – 2013

[abstract] [pdf] | [bib]

Abstract

This paper describes the speaker identification (SID) system developed by the Patrol team for the first phase of the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded communication channels. We present results using multiple SID systems differing mainly in the algorithm used for voice activity detection (VAD) and feature extraction. We show that (a) unsupervised VAD performs as well supervised methods in terms of downstream SID performance, (b) noise-robust feature extraction methods such as CFCCs out-perform MFCC front-ends on noisy audio, and (c) fusion of multiple systems provides 24% relative improvement in EER compared to the single best system when using a novel SVM-based fusion algorithm that uses side information such as gender, language, and channel id.
@{, author = {Oldrich Plchot and Spyros Matsoukas and Pavel Matejka and Najim Dehak and Jeff Ma and S. Cumani and O. Glembek and Hynek Hermansky and S.H. Mallidi and N. Mesgarani and R. Schwartz and M. Soufifar and Z.H. Tan and S. Thomas and B. Zhang and X. Zhou}, title = {Developing a Speaker Identification System for The Darpa Rats Project}, booktitle = {Proceedings of ICASSP 2013}, month = {May}, year = {2013}, address = {Vancouver, BC}, publisher = {IEEE}, pages = {6768 - 6772}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6638972&queryText%3DDeveloping+A+Speaker+Identification+System+For+The+DARPA+RATS+Project}, abstract = {This paper describes the speaker identification (SID) system developed by the Patrol team for the first phase of the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded communication channels. We present results using multiple SID systems differing mainly in the algorithm used for voice activity detection (VAD) and feature extraction. We show that (a) unsupervised VAD performs as well supervised methods in terms of downstream SID performance, (b) noise-robust feature extraction methods such as CFCCs out-perform MFCC front-ends on noisy audio, and (c) fusion of multiple systems provides 24% relative improvement in EER compared to the single best system when using a novel SVM-based fusion algorithm that uses side information such as gender, language, and channel id.} }

Filter-Bank Optimization for Frequency Domain Linear Prediction
Vijayaditya Peddinti and Hynek Hermansky
ICASSP'13 – 2013

[abstract] [pdf] | [bib]

Abstract

The sub-band Frequency Domain Linear Prediction (FDLP) technique estimates autoregressive models of Hilbert envelopes of subband signals, from segments of discrete cosine transform (DCT) of a speech signal, using windows. Shapes of the windows and their positions on the cosine transform of the signal determine implied filtering of the signal. Thus, the choices of shape, position and number of these windows can be critical for the performance of the FDLP technique. So far, we have used Gaussian or rectangular windows. In this paper asymmetric cochlear-like filters are being studied. Further, a frequency differentiation operation, that introduces an additional set of parameters describing local spectral slope in each frequency sub-band, is introduced to increase the robustness of sub-band envelopes in noise. The performance gains achieved by these changes are reported in a variety of additive noise conditions, with an average relative improvement of 8.04% in phoneme recognition accuracy.
@{, author = {Vijayaditya Peddinti and Hynek Hermansky}, title = {Filter-Bank Optimization for Frequency Domain Linear Prediction}, booktitle = {ICASSP'13}, month = {May}, year = {2013}, address = {Vancouver, BC}, publisher = {IEEE}, pages = {7102-7106}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6639040&queryText%3DFilter-Bank+Optimization+for+Frequency+Domain+Linear+Prediction}, abstract = {The sub-band Frequency Domain Linear Prediction (FDLP) technique estimates autoregressive models of Hilbert envelopes of subband signals, from segments of discrete cosine transform (DCT) of a speech signal, using windows. Shapes of the windows and their positions on the cosine transform of the signal determine implied filtering of the signal. Thus, the choices of shape, position and number of these windows can be critical for the performance of the FDLP technique. So far, we have used Gaussian or rectangular windows. In this paper asymmetric cochlear-like filters are being studied. Further, a frequency differentiation operation, that introduces an additional set of parameters describing local spectral slope in each frequency sub-band, is introduced to increase the robustness of sub-band envelopes in noise. The performance gains achieved by these changes are reported in a variety of additive noise conditions, with an average relative improvement of 8.04% in phoneme recognition accuracy.} }

Mean Temporal Distance: Predicting ASR Error from Temporal Properties of Speech Signal
Hynek Hermansky, Vijayaditya Peddinti and Ehsan Variani
2013

[abstract] [bib]

Abstract

Extending previous work on prediction of phoneme recognition error from unlabeled data that were corrupted by unpredictable factors, the current work investigates a simple but effective method of estimating ASR performance by computing a function M(Δt), which represents the mean distance between speech feature vectors evaluated over certain finite time interval, determined as a function of temporal distance Δt between the vectors. It is shown that M(Δt) is a function of signal-to-noise ratio of speech signal. Comparing M(Δt) curves, derived on data used for training of the classifier, and on test utterances, allows for predicting error on the test data. Another interesting observation is that M(Δt) remains approximately constant, as temporal separation Δt exceeds certain critical interval (about 200 ms), indicating the extent of coarticulation in speech sounds.
@{, author = {Hermansky, Hynek and Vijayaditya Peddinti and Ehsan Variani}, title = {Mean Temporal Distance: Predicting ASR Error from Temporal Properties of Speech Signal}, month = {May}, year = {2013}, address = {Vancouver, BC}, publisher = {IEEE}, abstract = {Extending previous work on prediction of phoneme recognition error from unlabeled data that were corrupted by unpredictable factors, the current work investigates a simple but effective method of estimating ASR performance by computing a function M(Δt), which represents the mean distance between speech feature vectors evaluated over certain finite time interval, determined as a function of temporal distance Δt between the vectors. It is shown that M(Δt) is a function of signal-to-noise ratio of speech signal. Comparing M(Δt) curves, derived on data used for training of the classifier, and on test utterances, allows for predicting error on the test data. Another interesting observation is that M(Δt) remains approximately constant, as temporal separation Δt exceeds certain critical interval (about 200 ms), indicating the extent of coarticulation in speech sounds.} }

Effect Of Filter Bandwidth and Spectral Sampling Rate of Analysis Filterbank on Automatic Phoneme Recognition
Feipeng Li and Hynek Hermansky
2013

[abstract] [pdf] | [bib]

Abstract

In this study we investigate the effect of filter bandwidth and spectral sampling rate of analysis filterbank for speech recognition. Two experiments are conducted to evaluate the performance of an automatic phoneme recognition system on clean speech and speech in noise as the filter bandwidth increases from 0.5 to 3.5 ERB and the spectral resolution changes from 1, 1.5, 2, 3, 4, to 6 samples per Bark. Results indicate that the optimum filter bandwidth varies for different speech sounds at different frequency ranges. A spectral sampling of 4 filters per Bark with the filter bandwidth being ≈ 1 ERB produces the best performance on average.
@{, author = {Feipeng Li and Hermansky, Hynek}, title = {Effect Of Filter Bandwidth and Spectral Sampling Rate of Analysis Filterbank on Automatic Phoneme Recognition}, month = {May}, year = {2013}, address = {Vancouver, BC}, publisher = {IEEE}, pages = {7121-7124}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6639044&queryText%3DEffect+Of+Filter+Bandwidth+and+Spectral+Sampling+Rate+of+Analysis+Filterbank+on+Automatic+Phoneme+Recognition}, abstract = {In this study we investigate the effect of filter bandwidth and spectral sampling rate of analysis filterbank for speech recognition. Two experiments are conducted to evaluate the performance of an automatic phoneme recognition system on clean speech and speech in noise as the filter bandwidth increases from 0.5 to 3.5 ERB and the spectral resolution changes from 1, 1.5, 2, 3, 4, to 6 samples per Bark. Results indicate that the optimum filter bandwidth varies for different speech sounds at different frequency ranges. A spectral sampling of 4 filters per Bark with the filter bandwidth being ≈ 1 ERB produces the best performance on average.} }

Deep Neural Network Features and Semi-Supervised Training for Low Resource Speech Recognition
Samuel Thomas, Michael Seltzer, Kenneth Church and Hynek Hermansky
2013

[abstract] [pdf] | [bib]

Abstract

We propose a new technique for training deep neural networks (DNNs) as data-driven feature front-ends for large vocabulary continuous speech recognition (LVCSR) in low resource settings. To circumvent the lack of sufficient training data for acoustic modeling in these scenarios, we use transcribed multilingual data and semi-supervised training to build the proposed feature front-ends. In our experiments, the proposed features provide an absolute improvement of 16% in a low-resource LVCSR setting with only one hour of in-domain training data. While close to three-fourths of these gains come from DNN-based features, the remaining are from semi-supervised training.
@{, author = {Samuel Thomas and Michael Seltzer and Kenneth Church and Hermansky, Hynek}, title = {Deep Neural Network Features and Semi-Supervised Training for Low Resource Speech Recognition}, month = {May}, year = {2013}, address = {Vancouver, BC}, publisher = {IEEE}, pages = {6704 - 6708}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6638959&queryText%3DDeep+Neural+Network+Features+and+Semi-Supervised+Training+for+Low+Resource+Speech+Recognition}, abstract = {We propose a new technique for training deep neural networks (DNNs) as data-driven feature front-ends for large vocabulary continuous speech recognition (LVCSR) in low resource settings. To circumvent the lack of sufficient training data for acoustic modeling in these scenarios, we use transcribed multilingual data and semi-supervised training to build the proposed feature front-ends. In our experiments, the proposed features provide an absolute improvement of 16% in a low-resource LVCSR setting with only one hour of in-domain training data. While close to three-fourths of these gains come from DNN-based features, the remaining are from semi-supervised training.} }

Dealing with Unknown Unknowns: Multi-stream Recognition of Speech
Hynek Hermansky
2013

[abstract] [pdf] | [bib]

Abstract

The paper discusses an approach for dealing with unexpected acoustic elements in speech. The approach is motivated by observations of human performance on such problems, which indicate the existence of multiple parallel processing streams in the human speech processing cognitive system, combined with the human ability to know when the correct information is being received. Some earlier relevant engineering approaches in multistream automatic recognition of speech (ASR) that aimed at processing of noisy speech and at dealing with unexpected out-of-vocabulary words are reviewed. The paper also reviews some currently active research in multistream ASR, focusing mainly on feedback-based techniques involving fusion of information between individual processing streams. The difference between the system behavior on its training data and during its operation is proposed as a substitute for the human ability of “knowing when knowing.” Most recent results indicate 9% relative improvement in error rates in phoneme recognition of high signal-to-noise ratio speech and as high as 30% relative improvements in moderate noise.
@{, author = {Hynek Hermansky}, title = {Dealing with Unknown Unknowns: Multi-stream Recognition of Speech}, month = {May}, year = {2013}, publisher = {IEEE}, pages = {1076 - 1088}, abstract = {The paper discusses an approach for dealing with unexpected acoustic elements in speech. The approach is motivated by observations of human performance on such problems, which indicate the existence of multiple parallel processing streams in the human speech processing cognitive system, combined with the human ability to know when the correct information is being received. Some earlier relevant engineering approaches in multistream automatic recognition of speech (ASR) that aimed at processing of noisy speech and at dealing with unexpected out-of-vocabulary words are reviewed. The paper also reviews some currently active research in multistream ASR, focusing mainly on feedback-based techniques involving fusion of information between individual processing streams. The difference between the system behavior on its training data and during its operation is proposed as a substitute for the human ability of “knowing when knowing.” Most recent results indicate 9% relative improvement in error rates in phoneme recognition of high signal-to-noise ratio speech and as high as 30% relative improvements in moderate noise.} }

Next Generation Storage for the HLTCOE
Scott Roberts
Technical Report 9, Human Language Technology Center of Excellence, Johns Hopkins University,
2013

[abstract] [pdf] | [bib]

Abstract

The explosion of unstructured data in high performance computing presents a challenge for existing storage architecture and design. We present a combination of hardware and software which addresses the storage needs of our center's compute cluster. We also demonstrate that at a constant total cost of ownership our proposed solution provides an order of magnitude better performance that then Johns Hopkins University's GrayWulf cluster and is two orders of magnitude faster than the center's existing storage array.
@techreport{roberts_tech:2013, author = {Roberts, Scott}, title = {Next Generation Storage for the HLTCOE}, number = {9}, institution = {Human Language Technology Center of Excellence, Johns Hopkins University}, month = {April}, year = {2013}, abstract = {The explosion of unstructured data in high performance computing presents a challenge for existing storage architecture and design. We present a combination of hardware and software which addresses the storage needs of our center's compute cluster. We also demonstrate that at a constant total cost of ownership our proposed solution provides an order of magnitude better performance that then Johns Hopkins University's GrayWulf cluster and is two orders of magnitude faster than the center's existing storage array.} }

Improved Speech-to-Text Translation with the Fisher and Callhome Spanish--English Speech Translation Corpus
Matt Post, Gaurav Kumar, Adam Lopez, Damianos Karakos, Chris Callison-Burch and Sanjeev Khudanpur
Proceedings of the International Workshop on Spoken Language Translation (IWSLT) – 2013

[pdf] | [bib]

@inproceedings{post2013improved, author = {Post, Matt and Gaurav Kumar and Lopez, Adam and Karakos, Damianos and Callison-Burch, Chris and Khudanpur, Sanjeev}, title = {Improved Speech-to-Text Translation with the Fisher and Callhome Spanish--English Speech Translation Corpus}, booktitle = {Proceedings of the International Workshop on Spoken Language Translation (IWSLT)}, month = {December}, year = {2013}, address = {Heidelberg, Germany} }

Beyond Bitext: Five open problems in machine translation
Adam Lopez and Matt Post
Twenty Years of Bitext – 2013

[pdf] | [bib]

@inproceedings{lopez2013beyond, author = {Lopez, Adam and Post, Matt}, title = {Beyond Bitext: Five open problems in machine translation}, booktitle = {Twenty Years of Bitext}, month = {October}, year = {2013}, address = {Seattle, Washington, USA} }

Answer Extraction as Sequence Tagging with Tree Edit Distance
Xuchen Yao, Benjamin Van Durme, Peter Clark and Chris Callison-Burch
North American Chapter of the Association for Computational Linguistics (NAACL) – 2013

[bib]

@inproceedings{, author = {Xuchen Yao and Van Durme, Benjamin and Peter Clark and Callison-Burch, Chris}, title = {Answer Extraction as Sequence Tagging with Tree Edit Distance}, booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2013}, url = {http://aclweb.org/anthology/N/N13/N13-1106.pdf} }

Generating Expressions that Refer to Visible Objects
Margaret Mitchell, Kees van Deemter and Ehud Reiter
North American Chapter of the Association for Computational Linguistics (NAACL) – 2013

[bib]

@inproceedings{, author = {Mitchell, Margaret and Kees van Deemter and Ehud Reiter}, title = {Generating Expressions that Refer to Visible Objects}, booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2013}, url = {http://www.m-mitchell.com/papers/MitchellEtAl-13-VisObjects.pdf} }

Massively Parallel Suffix Array Queries and On-Demand Phrase Extraction for Statistical Machine Translation Using GPUs
Hua He, Jimmy Lin and Adam Lopez
North American Chapter of the Association for Computational Linguistics (NAACL) – 2013

[bib]

@inproceedings{, author = {Hua He and Jimmy Lin and Lopez, Adam}, title = {Massively Parallel Suffix Array Queries and On-Demand Phrase Extraction for Statistical Machine Translation Using GPUs}, booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2013}, url = {http://aclweb.org/anthology/N/N13/N13-1033.pdf} }

Supervised Bilingual Lexicon Induction with Multiple Monolingual Signals
Ann Irvine and Chris Callison-Burch
North American Chapter of the Association for Computational Linguistics (NAACL) – 2013

[bib]

@inproceedings{, author = {Irvine, Ann and Callison-Burch, Chris}, title = {Supervised Bilingual Lexicon Induction with Multiple Monolingual Signals}, booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2013}, url = {http://aclweb.org/anthology//N/N13/N13-1056.pdf} }

PPDB: The Paraphrase Database
Juri Ganitkevitch, Benjamin Van Durme and Chris Callison-Burch
North American Chapter of the Association for Computational Linguistics (NAACL) – 2013

[bib]

@inproceedings{, author = {Juri Ganitkevitch and Van Durme, Benjamin and Callison-Burch, Chris}, title = {PPDB: The Paraphrase Database}, booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2013}, url = {http://aclweb.org/anthology/N/N13/N13-1092.pdf} }

Improving the Quality of Minority Class Identification in Dialog Act Tagging
Adinoyi Omuya, Vinodkumar Prabhakaran and Owen Rambow
North American Chapter of the Association for Computational Linguistics (NAACL) – 2013

[bib]

@inproceedings{, author = {Adinoyi Omuya and Vinodkumar Prabhakaran and Owen Rambow}, title = {Improving the Quality of Minority Class Identification in Dialog Act Tagging}, booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2013}, url = {http://www.newdesign.aclweb.org/anthology-new/N/N13/N13-1099.pdf} }

Broadly Improving User Classification via Communication-Based Name and Location Clustering on Twitter
Shane Bergsma, Mark Dredze, Benjamin Van Durme, Theresa Wilson and David Yarowsky
North American Chapter of the Association for Computational Linguistics (NAACL) – 2013

[bib]

@inproceedings{bergsma:2013, author = {Bergsma, Shane and Dredze, Mark and Van Durme, Benjamin and Wilson, Theresa and Yarowsky, David}, title = {Broadly Improving User Classification via Communication-Based Name and Location Clustering on Twitter}, booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2013}, url = {http://www.cs.jhu.edu/~vandurme/papers/broadly-improving-user-classfication-via-communication-based-name-and-location-clustering-on-twitter.pdf} }

What's in a Domain? Multi-Domain Learning for Multi-Attribute Data
Mahesh Joshi, Mark Dredze, William Cohen and Carolyn P. Rose
North American Chapter of the Association for Computational Linguistics (NAACL) – 2013

[bib]

@inproceedings{joshi:2013, author = {Mahesh Joshi and Dredze, Mark and William Cohen and Carolyn P. Rose}, title = {What's in a Domain? Multi-Domain Learning for Multi-Attribute Data}, booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2013}, url = {http://aclweb.org/anthology/N/N13/N13-1080.pdf} }

Separating Fact from Fear: Tracking Flu Infections on Twitter
Alex Lamb, Michael Paul and Mark Dredze
North American Chapter of the Association for Computational Linguistics (NAACL) – 2013

[bib]

@inproceedings{lamb:2013, author = {Alex Lamb and Michael Paul and Dredze, Mark}, title = {Separating Fact from Fear: Tracking Flu Infections on Twitter}, booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2013}, url = {http://www.cs.jhu.edu/~mdredze/publications/naacl_2013_flu.pdf} }

Drug Extraction from the Web: Summarizing Drug Experiences with Multi-Dimensional Topic Models
Michael Paul and Mark Dredze
North American Chapter of the Association for Computational Linguistics (NAACL) – 2013

[bib]

@inproceedings{Paul:2013, author = {Michael Paul and Dredze, Mark}, title = {Drug Extraction from the Web: Summarizing Drug Experiences with Multi-Dimensional Topic Models}, booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2013}, url = {http://www.cs.jhu.edu/~mdredze/publications/naacl_2013_drugs.pdf} }

Estimating Confusions in the ASR Channel for Improved Topic-based Language Model Adaptation
Damianos Karakos, Mark Dredze and Sanjeev Khudanpur
Technical Report 8, Human Language Technology Center of Excellence, Johns Hopkins University,
2013

[abstract] [pdf] | [bib]

Abstract

Human language is a combination of elemental languages/domains/styles that change across and sometimes within discourses. Language models, which play a crucial role in speech recognizers and machine translation systems, are particularly sensitive to such changes, unless some form of adaptation takes place. One approach to speech language model adaptation is self-training, in which a language model’s parameters are tuned based on automatically transcribed audio. However, transcription errors can misguide self-training, particularly in challenging settings such as conversational speech. In this work, we propose a model that considers the confusions (errors) of the ASR channel. By modeling the likely confusions in the ASR output instead of using just the 1-best, we improve self-training efficacy by obtaining a more reliable reference transcription estimate. We demonstrate improved topic-based language modeling adaptation results over both 1-best and lattice self-training using our ASR channel confusion estimates on telephone conversations.
@techreport{karakos_tech:2013, author = {Karakos, Damianos and Dredze, Mark and Khudanpur, Sanjeev}, title = {Estimating Confusions in the ASR Channel for Improved Topic-based Language Model Adaptation}, number = {8}, institution = {Human Language Technology Center of Excellence, Johns Hopkins University}, year = {2013}, abstract = {Human language is a combination of elemental languages/domains/styles that change across and sometimes within discourses. Language models, which play a crucial role in speech recognizers and machine translation systems, are particularly sensitive to such changes, unless some form of adaptation takes place. One approach to speech language model adaptation is self-training, in which a language model’s parameters are tuned based on automatically transcribed audio. However, transcription errors can misguide self-training, particularly in challenging settings such as conversational speech. In this work, we propose a model that considers the confusions (errors) of the ASR channel. By modeling the likely confusions in the ASR output instead of using just the 1-best, we improve self-training efficacy by obtaining a more reliable reference transcription estimate. We demonstrate improved topic-based language modeling adaptation results over both 1-best and lattice self-training using our ASR channel confusion estimates on telephone conversations.} }

Sustained Firing of Model Central Auditory Neurons Yields a Discriminative Spectro-temporal Representation for Natural Sounds
Michael A. Carlin and Mounya Elhilali
PLoS Computational Biology – 2013

[bib]

@article{Carlin2013, author = {Carlin, Michael and Mounya Elhilali}, title = {Sustained Firing of Model Central Auditory Neurons Yields a Discriminative Spectro-temporal Representation for Natural Sounds}, year = {2013}, url = {http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002982} }

Nonconvex Global Optimization for Latent Variable Models
Matthew R. Gormley and Jason Eisner
Association for Computational Linguistics (ACL) – 2013

[bib]

@inproceedings{gormley-eisner:2013, author = {Matthew R. Gormley and Eisner, Jason}, title = {Nonconvex Global Optimization for Latent Variable Models}, booktitle = {Association for Computational Linguistics (ACL)}, year = {2013}, url = {http://aclweb.org/anthology//P/P13/P13-1044.pdf} }

Learning to translate with products of novices: a suite of open-ended challenge problems for teaching MT
Adam Lopez, Matt Post, Chris Callison-Burch, Jonathan Weese, Juri Ganitkevitch, Narges Ahmidi, Olivia Buzek, Leah Hanson, Beenish Jamil, Matthias Lee, Ya-Ting Lin, Henry Pao, Fatima Rivera, Leili Shahriyari, Debu Sinha, Adam Teichert, Stephen Wampler, Michael Weinberger, Daguang Xu, Lin Yang and Shang Zhao
Transactions of the Association for Computational Linguistics – 2013

[bib]

@article{Lopez+etal:2013:tacl:mt-class, author = {Lopez, Adam and Post, Matt and Callison-Burch, Chris and Jonathan Weese and Juri Ganitkevitch and Narges Ahmidi and Buzek, Olivia and Leah Hanson and Beenish Jamil and Matthias Lee and Ya-Ting Lin and Henry Pao and Fatima Rivera and Leili Shahriyari and Debu Sinha and Adam Teichert and Stephen Wampler and Michael Weinberger and Daguang Xu and Lin Yang and Shang Zhao}, title = {Learning to translate with products of novices: a suite of open-ended challenge problems for teaching MT}, year = {2013}, url = {https://aclweb.org/anthology/Q/Q13/Q13-1014.pdf} }

Dirt Cheap Web-Scale Parallel Text from the Common Crawl
Jason R Smith, Herve Saint-Amand, Magdalena Plamada, Philipp Koehn, Chris Callison-Burch and Adam Lopez
Association for Computational Linguistics (ACL) – 2013

[bib]

@inproceedings{Smith+etal:2013:acl, author = {Smith, Jason and Herve Saint-Amand and Magdalena Plamada and Philipp Koehn and Callison-Burch, Chris and Lopez, Adam}, title = {Dirt Cheap Web-Scale Parallel Text from the Common Crawl}, booktitle = {Association for Computational Linguistics (ACL)}, year = {2013}, url = {http://aclweb.org/anthology/P/P13/P13-1135.pdf} }

KELVIN: a tool for automated knowledge base construction
Paul McNamee, James Mayfield, Tim Finin, , Dawn Lawrie, Tan Xu and Douglas W. Oard
North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstration Session (NAACL-HLT) – 2013

[bib]

@{, author = {McNamee, Paul and Mayfield, James and Finin, Tim and and Lawrie, Dawn and Tan Xu and Douglas W. Oard}, title = {KELVIN: a tool for automated knowledge base construction}, booktitle = {North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstration Session (NAACL-HLT)}, year = {2013}, url = {http://aclweb.org/anthology//N/N13/N13-3008.pdf} }

Using Conceptual Class Attributes to Characterize Social Media Users
Shane Bergsma and Benjamin Van Durme
Association for Computational Linguistics (ACL) – 2013

[bib]

@{, author = {Bergsma, Shane and Van Durme, Benjamin}, title = {Using Conceptual Class Attributes to Characterize Social Media Users}, booktitle = {Association for Computational Linguistics (ACL)}, year = {2013}, url = {http://aclweb.org/anthology//P/P13/P13-1070.pdf} }

SenseSpotting: Never let your parallel data tie you to an old domain
Marine Carpuat, Hal Daume III, Katharine Henry, Ann Irvine, Jagadeesh Jagarlamudi and Rachel Rudinger
Association for Computational Linguistics (ACL) – 2013

[bib]

@{, author = {Marine Carpuat and Hal Daume III and Katharine Henry and Irvine, Ann and Jagadeesh Jagarlamudi and Rachel Rudinger}, title = {SenseSpotting: Never let your parallel data tie you to an old domain}, booktitle = {Association for Computational Linguistics (ACL)}, year = {2013}, url = {http://aclweb.org/anthology/P/P13/P13-1141.pdf} }

Lightly Supervised Learning of Procedural Dialog Systems
Svitlana Volkova, Pallavi Choudhury, Chris Quirk, Bill Dolan and Luke Zettlemoyer
Association for Computational Linguistics (ACL) – 2013

[bib]

@inproceedings{Volkova_2013_ACL, author = {Volkova, Svitlana and Pallavi Choudhury and Chris Quirk and Bill Dolan and Luke Zettlemoyer}, title = {Lightly Supervised Learning of Procedural Dialog Systems}, booktitle = {Association for Computational Linguistics (ACL)}, year = {2013}, url = {http://aclweb.org/anthology//P/P13/P13-1164.pdf} }

Learning to Relate Literal and Sentimental Descriptions of Visual Properties
Mark Yatskar, Svitlana Volkova, Alsi Celikyilmaz, Bill Dolan and Luke Zettlemoyer
North American Chapter of the Association for Computational Linguistics (NAACL) – 2013

[bib]

@inproceedings{Volkova_2013_NAACL, author = {Mark Yatskar and Volkova, Svitlana and Alsi Celikyilmaz and Bill Dolan and Luke Zettlemoyer}, title = {Learning to Relate Literal and Sentimental Descriptions of Visual Properties}, booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2013}, url = {http://aclweb.org/anthology/N/N13/N13-1043.pdf} }

Supervector Bayesian Speaker Comparison
Bengt J. Borgstro ̈m and Alan McCree
International Conference on Acoustics, Speech, and Signal Processing (ICASSP) – 2013

[pdf] | [bib]

@{, author = {Bengt J. Borgstro ̈m and McCree, Alan}, title = {Supervector Bayesian Speaker Comparison}, booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {2013} }

Discriminatively Trained Bayesian Speaker Comparison of I-Vectors
Bengt J. Borgstro ̈m and Alan McCree
International Conference on Acoustics, Speech, and Signal Processing (ICASSP) – 2013

[pdf] | [bib]

@{, author = {Bengt J. Borgstro ̈m and McCree, Alan}, title = {Discriminatively Trained Bayesian Speaker Comparison of I-Vectors}, booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {2013} }

UMBC EBIQUITY-CORE: Semantic Textual Similarity Systems
Lushan Han, Abhay L. Kashyap, Tim Finin, James Mayfield and Jonathan Weese
Joint Conference on Lexical and Computational Semantics (*SEM) – 2013

[abstract] [pdf] | [bib]

Abstract

We describe three semantic text similarity systems developed for the *SEM 2013 STS shared task and the results of the corresponding three runs. All of them used a word similarity feature that combined LSA word similarity and WordNet knowledge. The first run, which achieved the top mean score on the task of all the submissions, used a simple term alignment algorithm. The other two runs, ranked second and fourth, used SVM models to combine a larger sets of features.
@{, author = {Lushan Han and Abhay L. Kashyap and Finin, Tim and Mayfield, James and Jonathan Weese}, title = {UMBC EBIQUITY-CORE: Semantic Textual Similarity Systems}, booktitle = {Joint Conference on Lexical and Computational Semantics (*SEM)}, year = {2013}, abstract = {We describe three semantic text similarity systems developed for the *SEM 2013 STS shared task and the results of the corresponding three runs. All of them used a word similarity feature that combined LSA word similarity and WordNet knowledge. The first run, which achieved the top mean score on the task of all the submissions, used a simple term alignment algorithm. The other two runs, ranked second and fourth, used SVM models to combine a larger sets of features.} }

Sub-Lexical and Contextual Modeling of Out-of-Vocabulary Words in Speech Recognition
Carolina Parada, Mark Dredze, Abhinav Sethy and Ariya Rastrow
Technical Report 10, Human Language Technology Center of Excellence, Johns Hopkins University,
2013

[abstract] [pdf] | [bib]

Abstract

Large vocabulary speech recognition systems fail to recognize words beyond their vocabulary, many of which are information rich terms, like named entities or foreign words. Hybrid word/sub-word systems solve this problem by adding sub-word units to large vocabulary word based systems; new words can then be represented by combinations of sub-word units. We present a novel probabilistic model to learn the sub-word lexicon optimized for a given task. We consider the task of Out Of vocabulary (OOV) word detection, which relies on output from a hybrid system. We combine the proposed hybrid system with confidence based metrics to improve OOV detection performance. Previous work address OOV detection as a binary classification task, where each region is independently classified using local information. We propose to treat OOV detection as a sequence labeling problem, and we show that 1) jointly predicting out-of-vocabulary regions, 2) including contextual information from each region, and 3) learning sub-lexical units optimized for this task, leads to substantial improvements with respect to state-of-the-art on an English Broadcast News and MIT Lectures task.
@techreport{, author = {Carolina Parada and Dredze, Mark and Abhinav Sethy and Ariya Rastrow}, title = {Sub-Lexical and Contextual Modeling of Out-of-Vocabulary Words in Speech Recognition}, number = {10}, institution = {Human Language Technology Center of Excellence, Johns Hopkins University}, year = {2013}, abstract = {Large vocabulary speech recognition systems fail to recognize words beyond their vocabulary, many of which are information rich terms, like named entities or foreign words. Hybrid word/sub-word systems solve this problem by adding sub-word units to large vocabulary word based systems; new words can then be represented by combinations of sub-word units. We present a novel probabilistic model to learn the sub-word lexicon optimized for a given task. We consider the task of Out Of vocabulary (OOV) word detection, which relies on output from a hybrid system. We combine the proposed hybrid system with confidence based metrics to improve OOV detection performance. Previous work address OOV detection as a binary classification task, where each region is independently classified using local information. We propose to treat OOV detection as a sequence labeling problem, and we show that 1) jointly predicting out-of-vocabulary regions, 2) including contextual information from each region, and 3) learning sub-lexical units optimized for this task, leads to substantial improvements with respect to state-of-the-art on an English Broadcast News and MIT Lectures task.} }

PARMA: A Predicate Argument Aligner
Travis Wolfe, Benjamin Van Durme, Mark Dredze, Nicholas Andrews, Charley Beller, Chris Callison-Burch, Jay DeYoung, Justin Snyder, Jonathan Weese, Tan Xu and Xuchen Yao
Association for Computational Linguistics (ACL) – 2013

[bib]

@inproceedings{Wolfe:2013lr, author = {Wolfe, Travis and Van Durme, Benjamin and Dredze, Mark and Andrews, Nicholas and Charley Beller and Callison-Burch, Chris and Jay DeYoung and Justin Snyder and Jonathan Weese and Tan Xu and Xuchen Yao}, title = {PARMA: A Predicate Argument Aligner}, booktitle = {Association for Computational Linguistics (ACL)}, year = {2013}, url = {http://aclweb.org/anthology//P/P13/P13-2012.pdf} }

Explicit and Implicit Syntactic Features for Text Classification
Matt Post and Shane Bergsma
Association for Computational Linguistics (ACL) – 2013

[bib]

@inproceedings{, author = {Post, Matt and Bergsma, Shane}, title = {Explicit and Implicit Syntactic Features for Text Classification}, booktitle = {Association for Computational Linguistics (ACL)}, year = {2013}, url = {http://aclweb.org/anthology//P/P13/P13-2150.pdf} }

Frequency Offset Correction in Speech without Detecting Pitch
Pascal Clark, Sri Harish Mallidi, Aren Jansen and Hynek Hermansky
International Conference on Acoustics, Speech, and Signal Processing (ICASSP) – 2013

[pdf] | [bib]

@inproceedings{, author = {Clark, Pascal and Sri Harish Mallidi and Jansen, Aren and Hermansky, Hynek}, title = {Frequency Offset Correction in Speech without Detecting Pitch}, booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {2013} }

Complementary envelope estimation for frequency-modulated random signals
Pascal Clark, Ivars Kirsteins and Les Atlas
International Conference on Acoustics, Speech, and Signal Processing (ICASSP) – 2013

[pdf] | [bib]

@inproceedings{, author = {Clark, Pascal and Ivars Kirsteins and Les Atlas}, title = {Complementary envelope estimation for frequency-modulated random signals}, booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {2013} }

A Lightweight and High Performance Monolingual Word Aligner
Xuchen Yao, Benjamin Van Durme, Peter Clark and Chris Callison-Burch
Association for Computational Linguistics (ACL) – 2013

[bib]

@inproceedings{yao-EtAl:2013:ACL, author = {Xuchen Yao and Van Durme, Benjamin and Peter Clark and Callison-Burch, Chris}, title = {A Lightweight and High Performance Monolingual Word Aligner}, booktitle = {Association for Computational Linguistics (ACL)}, year = {2013}, address = {Sofia, Bulgaria}, publisher = {Association for Computational Linguistics}, url = {http://aclweb.org/anthology//P/P13/P13-2123.pdf} }

Arabic Dialect Identification
Omar F Zaidan and Chris Callison-Burch
Computational Linguistics – 2013

[bib]

@article{zaidan-callisonburch:CL:2013, author = {Omar F Zaidan and Callison-Burch, Chris}, title = {Arabic Dialect Identification}, year = {2013}, url = {https://www.cs.jhu.edu/~ccb/publications/arabic-dialect-id.pdf} }

Automatic Coupling of Answer Extraction and Information Retrieval
Xuchen Yao, Benjamin Van Durme and Peter Clark
Association for Computational Linguistics (ACL) – 2013

[bib]

@inproceedings{yao1-EtAl:2013:ACL, author = {Xuchen Yao and Van Durme, Benjamin and Peter Clark}, title = {Automatic Coupling of Answer Extraction and Information Retrieval}, booktitle = {Association for Computational Linguistics (ACL)}, year = {2013}, address = {Sofia, Bulgaria}, publisher = {Association for Computational Linguistics}, url = {http://aclweb.org/anthology//P/P13/P13-2029.pdf} }

A Symmetric Kernel Partial Least Squares Framework for Speaker Recognition
Balaji Vasan Srinivasa, Yuancheng Luo, Daniel Garcia-Romero, Dmitry N. Zotkin and Ramani Duraiswami
IEEE Transactions on Audio, Speech, and Language Processing – 2013

[bib]

@article{6480796, author = {Balaji Vasan Srinivasa and Yuancheng Luo and Garcia-Romero, Daniel and Dmitry N. Zotkin and Ramani Duraiswami}, title = {A Symmetric Kernel Partial Least Squares Framework for Speaker Recognition}, year = {2013}, pages = {1415-1423}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6480796} }

Subspace-constrained Supervector PLDA for Speaker Verification
Daniel Garcia-Romero and Alan McCree
International Speech Communication Association (INTERSPEECH) – 2013

[bib]

@inproceedings{dgr-IS13, author = {Garcia-Romero, Daniel and McCree, Alan}, title = {Subspace-constrained Supervector PLDA for Speaker Verification}, booktitle = {International Speech Communication Association (INTERSPEECH)}, year = {2013} }

Nerit: Named Entity Recognition for Informal Text
David Etter, Francis Ferraro, Ryan Cotterell, Olivia Buzek and Benjamin Van Durme
Technical Report 11, Human Language Technology Center of Excellence, Johns Hopkins University,
2013

[abstract] [pdf] | [bib]

Abstract

We describe a multilingual named entity recognition system using language inde- pendent feature templates, designed for processing short, informal media arising from Twitter and other microblogging ser- vices. We crowdsource the annotation of tens of thousands of English and Spanish tweets and present classification results on this resource.
@techreport{, author = {David Etter and Francis Ferraro and Ryan Cotterell and Buzek, Olivia and Van Durme, Benjamin}, title = {Nerit: Named Entity Recognition for Informal Text}, number = {11}, institution = {Human Language Technology Center of Excellence, Johns Hopkins University}, year = {2013}, abstract = {We describe a multilingual named entity recognition system using language inde- pendent feature templates, designed for processing short, informal media arising from Twitter and other microblogging ser- vices. We crowdsource the annotation of tens of thousands of English and Spanish tweets and present classification results on this resource.} }

Open Domain Targeted Sentiment
Margaret Mitchell, Jacqui Aguilar, Theresa Wilson and Benjamin Van Durme
Empirical Methods in Natural Language Processing (EMNLP) – 2013

[bib]

@inproceedings{, author = {Mitchell, Margaret and Jacqui Aguilar and Wilson, Theresa and Van Durme, Benjamin}, title = {Open Domain Targeted Sentiment}, booktitle = {Empirical Methods in Natural Language Processing (EMNLP)}, year = {2013}, url = {http://aclweb.org/anthology/D/D13/D13-1171.pdf} }

Typicality and Object Reference
Margaret Mitchell, Ehud Reiter and Kees van Deemter
Annual Meeting of the Cognitive Science Society (CogSci) – 2013

[bib]

@inproceedings{, author = {Mitchell, Margaret and Ehud Reiter and Kees van Deemter}, title = {Typicality and Object Reference}, booktitle = {Annual Meeting of the Cognitive Science Society (CogSci)}, year = {2013}, url = {http://mindmodeling.org/cogsci2013/papers/0547/paper0547.pdf} }

Attributes in Visual Object Reference
Margaret Mitchell, Kees van Deemter and Ehud Reiter
PRE-CogSci – 2013

[bib]

@inproceedings{, author = {Mitchell, Margaret and Kees van Deemter and Ehud Reiter}, title = {Attributes in Visual Object Reference}, booktitle = {PRE-CogSci}, year = {2013}, url = {http://pre2013.uvt.nl/pdf/mitchell-reiter-vandeemter.pdf} }

Graphs and Spatial Relations in the Generation of Referring Expressions
Jette Viethen, Margaret Mitchell and Emiel Krahmer
European Workshop on Natural Language Generation (ENLG) – 2013

[bib]

@inproceedings{, author = {Jette Viethen and Mitchell, Margaret and Emiel Krahmer}, title = {Graphs and Spatial Relations in the Generation of Referring Expressions}, booktitle = {European Workshop on Natural Language Generation (ENLG)}, year = {2013}, url = {http://bridging.uvt.nl/pdf/viethen_mitchell_krahmer_enlg_2013.pdf} }

Semi-Markov Phrase-based Monolingual Alignment
Xuchen Yao, Benjamin Van Durme, Chris Callison-Burch and Peter Clark
Empirical Methods in Natural Language Processing (EMNLP) – 2013

[bib]

@inproceedings{yao-EtAl:2013:EMNLP, author = {Xuchen Yao and Van Durme, Benjamin and Callison-Burch, Chris and Peter Clark}, title = {Semi-Markov Phrase-based Monolingual Alignment}, booktitle = {Empirical Methods in Natural Language Processing (EMNLP)}, year = {2013}, address = {Seattle, Washington}, publisher = {Association for Computational Linguistics}, url = {http://cs.jhu.edu/~ccb/publications/semi-markov-phrase-based-monolingual-alignment.pdf} }

Intrinsic Spectral Analysis
Aren Jansen and Partha Niyogi
Signal Processing, IEEE Transactions on – 2013

[bib]

@article{jan_tsp13, author = {Jansen, Aren and Partha Niyogi}, title = {Intrinsic Spectral Analysis}, year = {2013}, publisher = {IEEE}, pages = {1698--1710}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06409472} }

Fixed-Dimensional Acoustic Embeddings of Variable-Length Segments in Low-Resource Settings
Keith Levin, Katharine Henry, Aren Jansen and Karen Livescu
ASRU – 2013

[bib]

@inproceedings{jan_asru13, author = {Levin, Keith and Katharine Henry and Jansen, Aren and Karen Livescu}, title = {Fixed-Dimensional Acoustic Embeddings of Variable-Length Segments in Low-Resource Settings}, booktitle = {ASRU}, year = {2013} }

Text-to-Speech Inspired Duration Modeling for Improved Whole-Word Acoustic Models
Keith Kintzley, Aren Jansen and Hynek Hermansky
Proceedings of Interspeech – 2013

[bib]

@inproceedings{jan_is13a, author = {Keith Kintzley and Jansen, Aren and Hermansky, Hynek}, title = {Text-to-Speech Inspired Duration Modeling for Improved Whole-Word Acoustic Models}, booktitle = {Proceedings of Interspeech}, year = {2013}, url = {http://www.isca-speech.org/archive/interspeech_2013/i13_1253.html} }

Semi-Supervised Manifold Learning Approaches for Spoken Term Verification
Atta Norouzian, Rick Rose and Aren Jansen
Interspeech – 2013

[bib]

@inproceedings{jan_is13b, author = {Atta Norouzian and Rose, Rick and Jansen, Aren}, title = {Semi-Supervised Manifold Learning Approaches for Spoken Term Verification}, booktitle = {Interspeech}, year = {2013}, url = {http://www.isca-speech.org/archive/interspeech_2013/i13_2594.html} }

Evaluating Speech Features with the Minimal-Pair ABX Task: Analysis of the Classical MFC/PLP Pipeline
Thomas Schatz, Vijayaditya Peddinti, Francis Bach, Aren Jansen, Hynek Hermansky and Emmanuel Dupoux
Proceedings of Interspeech – 2013

[bib]

@inproceedings{jan_is13c, author = {Thomas Schatz and Vijayaditya Peddinti and Francis Bach and Jansen, Aren and Hermansky, Hynek and Emmanuel Dupoux}, title = {Evaluating Speech Features with the Minimal-Pair ABX Task: Analysis of the Classical MFC/PLP Pipeline}, booktitle = {Proceedings of Interspeech}, year = {2013}, url = {http://www.isca-speech.org/archive/interspeech_2013/i13_1781.html} }

Weak Top-Down Constraints for Unsupervised Acoustic Model Training
Aren Jansen, Samuel Thomas and Hynek Hermansky
Proceedings of ICASSP – 2013

[bib]

@inproceedings{jan_icassp13a, author = {Jansen, Aren and Samuel Thomas and Hermansky, Hynek}, title = {Weak Top-Down Constraints for Unsupervised Acoustic Model Training}, booktitle = {Proceedings of ICASSP}, year = {2013}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6639241} }

A Summary of the 2012 CLSP Workshop on Zero Resource Speech Technologies and Models of Early Language Acquisition
Aren Jansen, Emmanuel Dupoux, Sharon Goldwater, Mark Johnson, Sanjeev Khudanpur, Kenneth Church, Naomi Feldman, Hynek Hermansky, Florian Metze, Richard Rose, Mike Seltzer, Pascal Clark, Ian McGraw, Balakrishnan Varadarajan, Erin Bennett, Benjamin Borschinger, Justin Chiu, Ewan Dunbar, Abdellah Fourtassi, David Harwath, Chia-ying Lee, Keith Levin, Atta Norouzian, Vijayaditya Peddinti, Rachael Richardson, Thomas Schatz and Samuel Thomas
Proceedings of ICASSP – 2013

[bib]

@inproceedings{jan_icassp13b, author = {Jansen, Aren and Emmanuel Dupoux and Sharon Goldwater and Mark Johnson and Khudanpur, Sanjeev and Kenneth Church and Naomi Feldman and Hermansky, Hynek and Florian Metze and Richard Rose and Mike Seltzer and Clark, Pascal and Ian McGraw and Balakrishnan Varadarajan and Erin Bennett and Benjamin Borschinger and Justin Chiu and Ewan Dunbar and Abdellah Fourtassi and David Harwath and Chia-ying Lee and Levin, Keith and Atta Norouzian and Vijayaditya Peddinti and Rachael Richardson and Thomas Schatz and Samuel Thomas}, title = {A Summary of the 2012 CLSP Workshop on Zero Resource Speech Technologies and Models of Early Language Acquisition}, booktitle = {Proceedings of ICASSP}, year = {2013}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6639245} }

Zero Resource Graph-Based Confidence Estimation for Open Vocabulary Spoken Term Detection
Atta Norouzian, Richard Rose, Sina Ghalehjegh and Aren Jansen
Proceedings of ICASSP – 2013

[bib]

@inproceedings{jan_icassp13d, author = {Atta Norouzian and Richard Rose and Sina Ghalehjegh and Jansen, Aren}, title = {Zero Resource Graph-Based Confidence Estimation for Open Vocabulary Spoken Term Detection}, booktitle = {Proceedings of ICASSP}, year = {2013}, url = {http://www.ece.mcgill.ca/~rrose1/papers/AttaRoseZeroResource_ICASSP13.pdf} }

Measuring Machine Translation Errors in New Domains
Ann Irvine, John Morgan, Marine Carpuat, Hal Daume III and Dragos Munteanu
Transactions of the Association for Computational Linguistics (TACL) – 2013

[bib]

@article{IrvineEtAlDAMTErrors, author = {Irvine, Ann and John Morgan and Marine Carpuat and Hal Daume III and Dragos Munteanu}, title = {Measuring Machine Translation Errors in New Domains}, year = {2013}, url = {https://aclweb.org/anthology/Q/Q13/Q13-1035.pdf} }

Monolingual Marginal Matching for Translation Model Adaptation
Ann Irvine, Chris Quirk and Hal Daume III
Empirical Methods in Natural Language Processing (EMNLP) – 2013

[bib]

@inproceedings{irvineQuirkDaumeEMNLP13, author = {Irvine, Ann and Chris Quirk and Hal Daume III}, title = {Monolingual Marginal Matching for Translation Model Adaptation}, booktitle = {Empirical Methods in Natural Language Processing (EMNLP)}, year = {2013}, url = {http://aclweb.org/anthology//D/D13/D13-1109.pdf} }

Bayesian Tree Substitution Grammars as a Usage-Based Approach
Matt Post and Daniel Gildea
Language and Speech – 2013

[bib]

@article{post2013bayesian, author = {Post, Matt and Daniel Gildea}, title = {Bayesian Tree Substitution Grammars as a Usage-Based Approach}, year = {2013}, pages = {291--308}, url = {http://las.sagepub.com/content/56/3/291.abstract} }

Long, Deep and Wide Artificial Neural Nets for Dealing with Unexpected Noise in Machine Recognition of Speech
Hynek Hermansky
Text, Speech, and Dialogue – 2013

[bib]

@inproceedings{hermansky2013long, author = {Hermansky, Hynek}, title = {Long, Deep and Wide Artificial Neural Nets for Dealing with Unexpected Noise in Machine Recognition of Speech}, booktitle = {Text, Speech, and Dialogue}, year = {2013}, pages = {14--21} }

Robust Speaker Recognition Using Spectro-Temporal Autoregressive Models
Sri Harish Mallidi, Sriram Ganapathy and Hynek Hermansky
2013

[pdf] | [bib]

@{, author = {Sri Harish Mallidi and Sriram Ganapathy and Hermansky, Hynek}, title = {Robust Speaker Recognition Using Spectro-Temporal Autoregressive Models}, year = {2013}, pages = {3689-3693}, url = {http://www.isca-speech.org/archive/interspeech_2013/i13_3689.html} }

Improvements in Language Indentification on the RATS Noisy Speech
Hynek Hermansky, Jeff Ma, Bing Zhang, Spyros Matsoukas, Sri Harish Mallidi and Feipeng Li
INTERSPEECH – 2013

[bib]

@{, author = {Hermansky, Hynek and Jeff Ma and Bing Zhang and Spyros Matsoukas and Sri Harish Mallidi and Feipeng Li}, title = {Improvements in Language Indentification on the RATS Noisy Speech}, booktitle = {INTERSPEECH}, year = {2013}, pages = {69-73}, url = {http://www.isca-speech.org/archive/interspeech_2013/i13_0069.html} }

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