2020
|
Hisamoto, Sorami; Post, Matt; Duh, Kevin Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System? Journal Article 2020. Links | BibTeX @article{Hisamoto2020,
title = {Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?},
author = {Sorami Hisamoto and Matt Post and Kevin Duh},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/hisamoto-etal-2020-membership.pdf},
year = {2020},
date = {2020-02-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2019
|
Maciejewski, Matthew; Sell, Gregory; Fujita, Yusuke; Garcia-Perera, Leibny Paola; Watanabe, Shinji; Khudanpur, Sanjeev Building Corpora for Single-Channel Speech Separation Across Multiple Domains Workshop 2019. Links | BibTeX @workshop{Maciejewski2019,
title = {Building Corpora for Single-Channel Speech Separation Across Multiple Domains},
author = {Matthew Maciejewski and Gregory Sell and Yusuke Fujita and Leibny Paola Garcia-Perera and Shinji Watanabe and Sanjeev Khudanpur},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/Maciejewski__Sell__Fujita__Garcia-Perera__Watanabe__Khudanpur_-_Analysis_of_Robustness_of_Deep_Single-Channel_Speech_Separation_Using_Corpora.pdf},
year = {2019},
date = {2019-10-20},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
|
Post, Matt; Ding, Shuoyang; Martindale, Marianna J; Wu, Winston An Exploration of Placeholding in Neural Machine Translation Journal Article 2019. Links | BibTeX @article{Post2019,
title = {An Exploration of Placeholding in Neural Machine Translation},
author = {Matt Post and Shuoyang Ding and Marianna J. Martindale and Winston Wu},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/W19-6618.pdf},
year = {2019},
date = {2019-08-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Lippincott, Tom Graph convolutional networks for exploring authorship hypotheses Proceeding 2019. Links | BibTeX @proceedings{Lippincott2019,
title = {Graph convolutional networks for exploring authorship hypotheses},
author = {Tom Lippincott},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/03/lippincott.pdf},
year = {2019},
date = {2019-06-03},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
|
Ondel, Lucas; Li, Ruizhi; Sell, Gregory; Hermansky, Hynek Deriving Spectro-Temporal Properties of Hearing from Speech Data Conference 2019. Links | BibTeX @conference{Ondel2019,
title = {Deriving Spectro-Temporal Properties of Hearing from Speech Data},
author = {Lucas Ondel and Ruizhi Li and Gregory Sell and Hynek Hermansky},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/Ondel__Li__Sell__Hermansky_-_Deriving_Spectro-Temporal_Properties_of_Hearing_from_Speech_Data.pdf},
year = {2019},
date = {2019-05-12},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
Stengel-Eskin, Elias; Su, Tzu-Ray; Post, Matt; Durme, Benjamin Van A Discriminative Neural Model for Cross-Lingual Word Alignment Journal Article 2019. Links | BibTeX @article{Stengel-Eskin2019,
title = {A Discriminative Neural Model for Cross-Lingual Word Alignment},
author = {Elias Stengel-Eskin and Tzu-Ray Su and Matt Post and Benjamin Van Durme},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/D19-1084.pdf},
year = {2019},
date = {2019-05-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Hu, Edward J; Khayrallah, Huda; Culkin, Ryan; Xia, Patrick; Chen, Tongfei; Post, Matt; Durme, Benjamin Van Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting Journal Article 2019. Links | BibTeX @article{Hu2019c,
title = {Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting},
author = {J. Edward Hu and Huda Khayrallah and Ryan Culkin and Patrick Xia and Tongfei Chen and Matt Post and Benjamin Van Durme},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/K19-1005-1.pdf},
year = {2019},
date = {2019-05-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Snyder, David; Villalba, Jesus; Chen, Nanxin; Povey, Daniel; Sell, Gregory; Dehak, Najim; Khudanpur, Sanjeev The JHU Speaker Recognition System for the VOiCES 2019 Challenge Proceeding 2019. Links | BibTeX @proceedings{Snyder2019,
title = {The JHU Speaker Recognition System for the VOiCES 2019 Challenge},
author = {David Snyder and Jesus Villalba and Nanxin Chen and Daniel Povey and Gregory Sell and Najim Dehak and Sanjeev Khudanpur},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/03/Snyder__Villalba__Chen__Povey__Sell__Dehak__Khudanpur_-_The_JHU_Speaker_Recognition_System_for_the_VOiCES_2019_Challenge.pdf},
year = {2019},
date = {2019-04-05},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
|
Villalba, Jesus; Chen, Nanxin; Snyder, David; Garcia-Romero, Daniel; McCree, Alan; Sell, Gregory; Borgstrom, Jonas; Richardson, Fred; Shon, Suwon; Grondin, Franc¸ois; Dehak, Reda; Garc´ıa-Perera, Leibny Paola; Povey, Daniel; Torres-Carrasquillo, Pedro A; Khudanpur, Sanjeev; Dehak, Najim State-of-the-art Speaker Recognition for Telephone and Video Speech: the JHU-MIT Submission for NIST SRE18 Conference 2019. Links | BibTeX @conference{Villalba2019,
title = {State-of-the-art Speaker Recognition for Telephone and Video Speech: the JHU-MIT Submission for NIST SRE18},
author = {Jesus Villalba and Nanxin Chen and David Snyder and Daniel Garcia-Romero and Alan McCree and Gregory Sell and Jonas Borgstrom and Fred Richardson and Suwon Shon and Franc¸ois Grondin and Reda Dehak and Leibny Paola Garc´ıa-Perera and Daniel Povey and Pedro A. Torres-Carrasquillo and Sanjeev Khudanpur and Najim Dehak},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/03/Villalba__Chen__Synder__Garcia-Romero__McCree__Sell__Borgstrom__Richardson__Shon__Grondin__Dehak__Garcia-Perera__Povey__Torres-Carrasquil-1.pdf},
year = {2019},
date = {2019-04-01},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
Hu, Edward J; Singh, Abhinav; Holzenberger, Nils; Post, Matt; Durme, Benjamin Van Diverse Paraphrasing via Sampling and Clustering Journal Article 2019. Links | BibTeX @article{Hu2019b,
title = {Diverse Paraphrasing via Sampling and Clustering},
author = {J. Edward Hu and Abhinav Singh and Nils Holzenberger and Matt Post and Benjamin Van Durme},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/K19-1005.pdf},
year = {2019},
date = {2019-04-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Li, Ruizhi; Sell, Gregory; Hermansky, Hynek Performance Monitoring for End-to-End Speech Recognition Proceeding 2019. Links | BibTeX @proceedings{Hermansky2019,
title = {Performance Monitoring for End-to-End Speech Recognition},
author = {Ruizhi Li and Gregory Sell and Hynek Hermansky},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/03/Li__Sell__Hermansky_-_Performance_Monitoring_for_End-to-End_Speech_Recognition.pdf},
year = {2019},
date = {2019-03-01},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
|
Etter, David; Rawls, Stephen; Carpenter, Cameron; Sell, Gregory A Synthetic Recipe for OCR Proceeding 2019. Links | BibTeX @proceedings{Etter2019,
title = {A Synthetic Recipe for OCR},
author = {David Etter and Stephen Rawls and Cameron Carpenter and Gregory Sell},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/03/Etter__Rawls__Carpenter__Sell_-_A_Synthetic_Recipe_for_OCR.pdf},
year = {2019},
date = {2019-03-01},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
|
Sell, Gregory; Etter, David; Garcia-Romero, Daniel; McCree, Alan Script Identification using Across- and Within-Image Distribution Estimation Proceeding 2019. Links | BibTeX @proceedings{Sell2019,
title = {Script Identification using Across- and Within-Image Distribution Estimation},
author = {Gregory Sell and David Etter and Daniel Garcia-Romero and Alan McCree},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/Sell__Etter__Garcia-Romero__McCree_-_Script_Identification_using_Across-_and_Within-Image_Distribution_Estimation.pdf},
year = {2019},
date = {2019-02-01},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
|
Hu, Edward J; Rudinger, Rachel; Post, Matt; Durme, Benjamin Van PARABANK: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-constrained Neural Journal Article 2019. Links | BibTeX @article{Hu2019,
title = {PARABANK: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-constrained Neural},
author = {J. Edward Hu and Rachel Rudinger and Matt Post and Benjamin Van Durme},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/2YXOZE-hu-etal-2019-parabank.pdf},
year = {2019},
date = {2019-01-27},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2018
|
Kothinti, Sandeep; Imoto, Keisuke; Chakrabarty, Debmalya; Sell, Gregory; Watanabe, Shinji; Elhilali, Mounya JOINT ACOUSTIC AND CLASS INFERENCE FOR WEAKLY SUPERVISED SOUND EVENT DETECTION Conference 2018. Links | BibTeX @conference{Kothinti2018,
title = {JOINT ACOUSTIC AND CLASS INFERENCE FOR WEAKLY SUPERVISED SOUND EVENT DETECTION},
author = {Sandeep Kothinti and Keisuke Imoto and Debmalya Chakrabarty and Gregory Sell and Shinji Watanabe and Mounya Elhilali},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/Kothinti__Imoto__Chakrabaarty__Sell__Watanable__Elhilali_-_Joint_Acoustic_and_Class_Inference_for_Weakly_Supervised_Sound_Event_Detection.pdf},
year = {2018},
date = {2018-11-01},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
Sell, Gregory; Snyder, David; McCree, Alan; Garcia-Romero, Daniel; Villalba, Jesus; ´
Maciejewski, Matthew; Manohar, Vimal; Dehak, Najim; Povey, Daniel; Watanabe, Shinji; Khudanpur, Sanjeev Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge Conference 2018. Links | BibTeX @conference{Sell2018,
title = {Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge},
author = {Gregory Sell and David Snyder and Alan McCree and Daniel Garcia-Romero and Jesus Villalba and Matthew ´
Maciejewski and Vimal Manohar and Najim Dehak and Daniel Povey and Shinji Watanabe and Sanjeev Khudanpur},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/IS18_dihard.pdf},
year = {2018},
date = {2018-09-02},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
Lippincott, Thomas Portable, layer-wise task performance monitoring for NLP models Proceeding 2018. Links | BibTeX @proceedings{Lippincott2018,
title = {Portable, layer-wise task performance monitoring for NLP models},
author = {Thomas Lippincott},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/vivisect.pdf},
year = {2018},
date = {2018-09-01},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
|
Lippincott, Tom; Carrell, Annabelle Observational Comparison of Geo-tagged and Randomly-drawn Tweets Journal Article 2018. Links | BibTeX @article{Lippincott2018b,
title = {Observational Comparison of Geo-tagged and Randomly-drawn Tweets},
author = {Tom Lippincott and Annabelle Carrell},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/geo.pdf},
year = {2018},
date = {2018-06-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Sell, Gregory; Duh, Kevin; Snyder, David; Etter, Dave; Garcia-Romero, Daniel Audio-Visual Person Recognition in Multimedia Data from the IARPA Janus Program Proceeding 2018. Links | BibTeX @proceedings{Sell2018b,
title = {Audio-Visual Person Recognition in Multimedia Data from the IARPA Janus Program},
author = {Gregory Sell and Kevin Duh and David Snyder and Dave Etter and Daniel Garcia-Romero},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/ICASSP18_Janus.pdf},
year = {2018},
date = {2018-01-01},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
|
2017
|
Khayrallah, Huda; Kumar, Gaurav; Duh, Kevin; Post, Matt; Koehn, Philipp Neural Lattice Rescoring for Phrase-based Machine Translation Conference 2017. Links | BibTeX @conference{Khayrallah2017,
title = {Neural Lattice Rescoring for Phrase-based Machine Translation},
author = {Huda Khayrallah and Gaurav Kumar and Kevin Duh and Matt Post and Philipp Koehn},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/I17-2004.pdf},
year = {2017},
date = {2017-05-01},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
2016
|
Lippincott, Tom; Durme, Benjamin Van Fluency detection on communication networks Proceeding 2016. Links | BibTeX @proceedings{Lippincott2016,
title = {Fluency detection on communication networks},
author = {Tom Lippincott and Benjamin Van Durme},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2020/04/fluency.pdf},
year = {2016},
date = {2016-11-01},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
|
Yung, Frances ; Duh, Kevin ; Komura, Taku ; Matsumoto, Yuji Modelling the Usage of Discourse Connectives as Rational Speech Acts Inproceedings Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning, pp. 302–313, Association for Computational Linguistics, Berlin, Germany, 2016. Links | BibTeX @inproceedings{yung16rational,
title = {Modelling the Usage of Discourse Connectives as Rational Speech Acts},
author = {Yung, Frances and Duh, Kevin and Komura, Taku and Matsumoto, Yuji},
url = {http://www.aclweb.org/anthology/K16-1030},
year = {2016},
date = {2016-08-01},
booktitle = {Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning},
pages = {302--313},
publisher = {Association for Computational Linguistics},
address = {Berlin, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Ding, Shuoyang ; Duh, Kevin ; Khayrallah, Huda ; Koehn, Philipp ; Post, Matt The JHU Machine Translation Systems for WMT 2016 Inproceedings Proceedings of the First Conference on Machine Translation, pp. 272–280, Association for Computational Linguistics, Berlin, Germany, 2016. Links | BibTeX @inproceedings{ding16wmt,
title = {The JHU Machine Translation Systems for WMT 2016},
author = {Ding, Shuoyang and Duh, Kevin and Khayrallah, Huda and Koehn, Philipp and Post, Matt},
url = {http://www.aclweb.org/anthology/W16-2310},
year = {2016},
date = {2016-08-01},
booktitle = {Proceedings of the First Conference on Machine Translation},
pages = {272--280},
publisher = {Association for Computational Linguistics},
address = {Berlin, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Yung, Frances ; Duh, Kevin ; Komura, Taku ; Matsumoto, Yuji Modelling the Interpretation of Discourse Connectives by Bayesian Pragmatics Inproceedings Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 531–536, Association for Computational Linguistics, Berlin, Germany, 2016. Links | BibTeX @inproceedings{yung16connectives,
title = {Modelling the Interpretation of Discourse Connectives by Bayesian Pragmatics},
author = {Yung, Frances and Duh, Kevin and Komura, Taku and Matsumoto, Yuji},
url = {http://anthology.aclweb.org/P16-2086},
year = {2016},
date = {2016-08-01},
booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
pages = {531--536},
publisher = {Association for Computational Linguistics},
address = {Berlin, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Chen, L; Vogelstein, J T; Lyzinski, V; Priebe, C E A joint graph inference case study: the c. elegans chemical and electrical connectomes Inproceedings Worm, Taylor & Francis 2016. BibTeX @inproceedings{chen2016joint,
title = {A joint graph inference case study: the c. elegans chemical and electrical connectomes},
author = {L. Chen and J. T. Vogelstein and V. Lyzinski and C. E. Priebe},
year = {2016},
date = {2016-01-01},
booktitle = {Worm},
volume = {5},
number = {2},
organization = {Taylor & Francis},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Sell, Gregory; McCree, Alan; Garcia-Romero, Daniel Priors for Speaker Counting and Diarization with AHC Inproceedings Proceedings of Interspeech, 2016. Abstract | Links | BibTeX @inproceedings{Sell_McCree_Garcia-Romero_2016A,
title = {Priors for Speaker Counting and Diarization with AHC},
author = {Gregory Sell and Alan McCree and Daniel Garcia-Romero},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2016/10/Sell_McCree_Garcia-Romero_2016A.pdf},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of Interspeech},
abstract = {Estimating the number of speakers in an audio segment is a necessary step in the process of speaker diarization, but current diarization algorithms do not explicitly define a prior probability on this estimation. This work proposes a process for including priors in speaker diarization with agglomerative hierarchical clustering (AHC). It is also shown that the exclusion of a prior with AHC is itself implicitly a prior, which is found to be geometric growth in the number of speakers. By using more sensible priors, we are able to demonstrate significantly improved robustness to calibration error for speaker counting and speaker diarization.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Estimating the number of speakers in an audio segment is a necessary step in the process of speaker diarization, but current diarization algorithms do not explicitly define a prior probability on this estimation. This work proposes a process for including priors in speaker diarization with agglomerative hierarchical clustering (AHC). It is also shown that the exclusion of a prior with AHC is itself implicitly a prior, which is found to be geometric growth in the number of speakers. By using more sensible priors, we are able to demonstrate significantly improved robustness to calibration error for speaker counting and speaker diarization. |
McCree, Alan; Sell, Gregory; Garcia-Romero, Daniel Augmented Data Training of Joint Acoustic/Phonotactic DNN i-vectors for NIST LRE15 Inproceedings Proceedings of Odyssey, 2016. Abstract | Links | BibTeX @inproceedings{McCree_Sell_Garcia-Romero_2016A,
title = {Augmented Data Training of Joint Acoustic/Phonotactic DNN i-vectors for NIST LRE15},
author = {Alan McCree and Gregory Sell and Daniel Garcia-Romero},
url = {https://hltcoe.jhu.edu/wp-content/uploads/2016/10/McCree_Sell_Garcia-Romero_2016A.pdf},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of Odyssey},
abstract = {This paper presents the JHU HLTCOE submission to the NIST 2015 Language Recognition Evaluation, including critical and novel algorithmic components, use of limited and augmented training data, and additional post-evaluation analysis and improvements. All of our systems used i-vectors based on Deep Neural Networks (DNNs) with discriminatively-trained Gaussian classifiers, and linear fusion was performed with duration-dependent scaling. A key innovation was the use of three different kinds of i-vectors: acoustic, phonotactic, and joint. In addition, data augmentation was used to overcome the limited training data of this evaluation. Post-evaluation analysis shows the benefits of these design decisions as well as further potential improvements.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents the JHU HLTCOE submission to the NIST 2015 Language Recognition Evaluation, including critical and novel algorithmic components, use of limited and augmented training data, and additional post-evaluation analysis and improvements. All of our systems used i-vectors based on Deep Neural Networks (DNNs) with discriminatively-trained Gaussian classifiers, and linear fusion was performed with duration-dependent scaling. A key innovation was the use of three different kinds of i-vectors: acoustic, phonotactic, and joint. In addition, data augmentation was used to overcome the limited training data of this evaluation. Post-evaluation analysis shows the benefits of these design decisions as well as further potential improvements. |
Lyzinski, Vince ; Tang, Minh ; Athreya, Avanti ; Park, Youngser ; Priebe, Carey E Community Detection and Classification in Hierarchical Stochastic Blockmodels Journal Article IEEE Transactions on Network Science and Engineering, accepted for publication , 2016. BibTeX @article{lyzinski2015community,
title = {Community Detection and Classification in Hierarchical Stochastic Blockmodels},
author = {Lyzinski, Vince and Tang, Minh and Athreya, Avanti and Park, Youngser and Priebe, Carey E},
year = {2016},
date = {2016-01-01},
journal = {IEEE Transactions on Network Science and Engineering},
volume = {accepted for publication},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Lyzinski, V Information Recovery in Shuffled Graphs via Graph Matching Journal Article arXiv preprint arXiv:1605.02315, 2016. BibTeX @article{lyzinski2016information,
title = {Information Recovery in Shuffled Graphs via Graph Matching},
author = {Lyzinski, V.},
year = {2016},
date = {2016-01-01},
journal = {arXiv preprint arXiv:1605.02315},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Lyzinski, V; Levin, K; Fishkind, D E; Priebe, C E On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching Journal Article Journal of Machine Learning Research, 17 (179), pp. 1–34, 2016. BibTeX @article{VN3,
title = {On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching},
author = {Lyzinski, V. and Levin, K. and Fishkind, D. E. and Priebe, C. E.},
year = {2016},
date = {2016-01-01},
journal = {Journal of Machine Learning Research},
volume = {17},
number = {179},
pages = {1--34},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Tang, M; Athreya, A; Sussman, D L; Lyzinski, V; Park, Y; Priebe, C E A semiparametric two-sample hypothesis testing problem for random dot product graphs Journal Article Journal of Computational and Graphical Statistics, Accepted for publication , 2016. BibTeX @article{MT2,
title = {A semiparametric two-sample hypothesis testing problem for random dot product graphs},
author = {M. Tang and A. Athreya and D. L. Sussman and V. Lyzinski and Y. Park and C. E. Priebe},
year = {2016},
date = {2016-01-01},
journal = {Journal of Computational and Graphical Statistics},
volume = {Accepted for publication},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Lyzinski, Vince ; Fishkind, Donniell E; Fiori, Marcelo ; Vogelstein, Joshua T; Priebe, Carey E; Sapiro, Guillermo Graph matching: Relax at your own risk Journal Article IEEE Transactions on Pattern Analysis and Machine Intelligence, 38 (1), pp. 60–73, 2016. BibTeX @article{rel,
title = {Graph matching: Relax at your own risk},
author = {Lyzinski, Vince and Fishkind, Donniell E and Fiori, Marcelo and Vogelstein, Joshua T and Priebe, Carey E and Sapiro, Guillermo},
year = {2016},
date = {2016-01-01},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {38},
number = {1},
pages = {60--73},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Zheng, D; Mhembere, D; Lyzinski, V; Vogelstein, J; Priebe, C E; Burns, R Semi-External Memory Sparse Matrix Multiplication on Billion-node Graphs in a Multicore Architecture Journal Article IEEE Transactions in Parallel and Distributed Systems, Accepted for publication , 2016. BibTeX @article{zheng2016semi,
title = {Semi-External Memory Sparse Matrix Multiplication on Billion-node Graphs in a Multicore Architecture},
author = {Zheng, D. and Mhembere, D. and Lyzinski, V. and Vogelstein, J. and Priebe, C. E. and Burns, R.},
year = {2016},
date = {2016-01-01},
journal = {IEEE Transactions in Parallel and Distributed Systems},
volume = {Accepted for publication},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Lyzinski, V; Park, Y; Priebe, C E; Trosset, M W Fast Embedding for JOFC Using the Raw Stress Criterion Journal Article Journal of Computational and Graphical Statistics, Accepted for publication , 2016. BibTeX @article{lyzinski2015fast,
title = {Fast Embedding for JOFC Using the Raw Stress Criterion},
author = {Lyzinski, V. and Park, Y. and Priebe, C. E. and Trosset, M. W.},
year = {2016},
date = {2016-01-01},
journal = {Journal of Computational and Graphical Statistics},
volume = {Accepted for publication},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Tanaka, Tomohiro; Moriya, Takafumi; Shinozaki, Takahiro; Watanabe, Shinji; Hori, Takaaki; Duh, Kevin Automated structure discovery and parameter tuning of neural network language model based on evolution strategy Inproceedings Proceedings of the 2016 IEEE Workshop on Spoken Language Technology, 2016. BibTeX @inproceedings{tanaka16evolution,
title = {Automated structure discovery and parameter tuning of neural network language model based on evolution strategy},
author = {Tomohiro Tanaka and Takafumi Moriya and Takahiro Shinozaki and Shinji Watanabe and Takaaki Hori and Kevin Duh},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of the 2016 IEEE Workshop on Spoken Language Technology},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Tsubaki, Masashi; Duh, Kevin; Shimbo, Masashi; Matsumoto, Yuji Non-Linear Similarity Learning for Compositionality Inproceedings AAAI Conference on Artificial Intelligence, 2016. Abstract | Links | BibTeX @inproceedings{tsubaki16nonlinear,
title = {Non-Linear Similarity Learning for Compositionality},
author = {Masashi Tsubaki and Kevin Duh and Masashi Shimbo and Yuji Matsumoto},
url = {http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12373},
year = {2016},
date = {2016-01-01},
booktitle = {AAAI Conference on Artificial Intelligence},
abstract = {Many NLP applications rely on the existence ofsimilarity measures over text data.Although word vector space modelsprovide good similarity measures between words,phrasal and sentential similarities derived from compositionof individual words remain as a difficult problem.In this paper, we propose a new method of ofnon-linear similarity learning for semantic compositionality.In this method, word representations are learnedthrough the similarity learning of sentencesin a high-dimensional space with kernel functions.On the task of predicting the semantic similarity oftwo sentences (SemEval 2014, Task 1),our method outperforms linear baselines,feature engineering approaches,recursive neural networks,and achieve competitive results with long short-term memory models.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Many NLP applications rely on the existence ofsimilarity measures over text data.Although word vector space modelsprovide good similarity measures between words,phrasal and sentential similarities derived from compositionof individual words remain as a difficult problem.In this paper, we propose a new method of ofnon-linear similarity learning for semantic compositionality.In this method, word representations are learnedthrough the similarity learning of sentencesin a high-dimensional space with kernel functions.On the task of predicting the semantic similarity oftwo sentences (SemEval 2014, Task 1),our method outperforms linear baselines,feature engineering approaches,recursive neural networks,and achieve competitive results with long short-term memory models. |
2015
|
Bojar, Ondřej ; Chatterjee, Rajen ; Federmann, Christian ; Haddow, Barry ; Huck, Matthias ; Hokamp, Chris ; Koehn, Philipp ; Logacheva, Varvara ; Monz, Christof ; Negri, Matteo ; Post, Matt ; Scarton, Carolina ; Specia, Lucia ; Turchi, Marco Findings of the 2015 Workshop on Statistical Machine Translation Inproceedings Proceedings of the Tenth Workshop on Statistical Machine Translation, pp. 1–46, Association for Computational Linguistics, Lisbon, Portugal, 2015. BibTeX @inproceedings{bojar-EtAl:2015:WMT,
title = {Findings of the 2015 Workshop on Statistical Machine Translation},
author = {Bojar, Ondřej and Chatterjee, Rajen and Federmann, Christian and Haddow, Barry and Huck, Matthias and Hokamp, Chris and Koehn, Philipp and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Scarton, Carolina and Specia, Lucia and Turchi, Marco},
year = {2015},
date = {2015-09-01},
booktitle = {Proceedings of the Tenth Workshop on Statistical Machine Translation},
pages = {1--46},
publisher = {Association for Computational Linguistics},
address = {Lisbon, Portugal},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Napoles, Courtney ; Sakaguchi, Keisuke ; Post, Matt ; Tetreault, Joel Ground Truth for Grammatical Error Correction Metrics Inproceedings Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 588–593, Association for Computational Linguistics, Beijing, China, 2015. BibTeX @inproceedings{napoles2015ground,
title = {Ground Truth for Grammatical Error Correction Metrics},
author = {Napoles, Courtney and Sakaguchi, Keisuke and Post, Matt and Tetreault, Joel},
year = {2015},
date = {2015-07-01},
booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
pages = {588--593},
publisher = {Association for Computational Linguistics},
address = {Beijing, China},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Coppersmith, Glen ; Dredze, Mark ; Harman, Craig ; Hollingshead, Kristy From ADHD to SAD: analyzing the language of mental health on Twitter through self-reported diagnoses Inproceedings NAACL Workshop on Computational Linguistics and Clinical Psychology, 2015. Abstract | BibTeX @inproceedings{coppersmith15a,
title = {From ADHD to SAD: analyzing the language of mental health on Twitter through self-reported diagnoses},
author = {Coppersmith, Glen and Dredze, Mark and Harman, Craig and Kristy Hollingshead},
year = {2015},
date = {2015-01-01},
booktitle = {NAACL Workshop on Computational Linguistics and Clinical Psychology},
abstract = {Many significant challenges exist for the mental health field, but one in particular is a lack of data available to guide research. Language provides a natural lens for studying mental health -- much existing work and therapy have strong linguistic components, so the creation of a large, varied, language-centric dataset could provide significant grist for the field of mental health research. We examine a broad range of mental health conditions in Twitter data by identifying self-reported statements of diagnosis. We systematically explore language differences between ten conditions with respect to the general population, and to each other. Our aim is to provide guidance and a roadmap for where deeper exploration is likely to be fruitful.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Many significant challenges exist for the mental health field, but one in particular is a lack of data available to guide research. Language provides a natural lens for studying mental health -- much existing work and therapy have strong linguistic components, so the creation of a large, varied, language-centric dataset could provide significant grist for the field of mental health research. We examine a broad range of mental health conditions in Twitter data by identifying self-reported statements of diagnosis. We systematically explore language differences between ten conditions with respect to the general population, and to each other. Our aim is to provide guidance and a roadmap for where deeper exploration is likely to be fruitful. |
Sylak-Glassman, John ; Kirov, Christo ; Post, Matt ; Que, Roger ; Yarowsky, David A Universal Feature Schema for Rich Morphological Annotation and Fine-Grained Cross-Lingual Part-of- Speech Tagging Incollection Systems and Frameworks for Computational Morphology, Springer, 2015. BibTeX @incollection{sylak-glassman2015a-universal,
title = {A Universal Feature Schema for Rich Morphological Annotation and Fine-Grained Cross-Lingual Part-of- Speech Tagging},
author = {Sylak-Glassman, John and Kirov, Christo and Post, Matt and Que, Roger and Yarowsky, David},
year = {2015},
date = {2015-01-01},
booktitle = {Systems and Frameworks for Computational Morphology},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
|
Post, Matt ; Cao, Yuan ; Kumar, Gaurav Joshua 6: A phrase-based and hierarchical statistical machine translation system Journal Article The Prague Bulletin of Mathematical Linguistics, 2015. BibTeX @article{post2015joshua,
title = {Joshua 6: A phrase-based and hierarchical statistical machine translation system},
author = {Post, Matt and Cao, Yuan and Kumar, Gaurav},
year = {2015},
date = {2015-01-01},
journal = {The Prague Bulletin of Mathematical Linguistics},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Smith, Michael; Broniatowski, David; Paul, Michael; Dredze, Mark Tracking Public Awareness of Influenza through Twitter Inproceedings 3rd International Conference on Digital Disease Detection (DDD), 2015. BibTeX @inproceedings{Smith:2015mz,
title = {Tracking Public Awareness of Influenza through Twitter},
author = {Michael Smith and David Broniatowski and Michael Paul and Dredze, Mark},
year = {2015},
date = {2015-01-01},
booktitle = {3rd International Conference on Digital Disease Detection (DDD)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Paul, Michael; Dredze, Mark SPRITE: Generalizing Topic Models with Structured Priors Journal Article 2015. Abstract | Links | BibTeX @article{Paul:2015sf,
title = {SPRITE: Generalizing Topic Models with Structured Priors},
author = {Michael Paul and Dredze, Mark},
url = {https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/403/106},
year = {2015},
date = {2015-01-01},
abstract = {We introduce SPRITE, a family of topic models that incorporates structure into model priors as a function of underlying components. The structured priors can be constrained to model topic hierarchies, factorizations, correlations, and supervision, allowing SPRITE to be tailored to particular settings. We demonstrate this flexibility by constructing a SPRITE-based model to jointly infer topic hierarchies and author perspective, which we apply to corpora of political debates and online reviews. We show that the model learns intuitive topics, outperforming several other topic models at predictive tasks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We introduce SPRITE, a family of topic models that incorporates structure into model priors as a function of underlying components. The structured priors can be constrained to model topic hierarchies, factorizations, correlations, and supervision, allowing SPRITE to be tailored to particular settings. We demonstrate this flexibility by constructing a SPRITE-based model to jointly infer topic hierarchies and author perspective, which we apply to corpora of political debates and online reviews. We show that the model learns intuitive topics, outperforming several other topic models at predictive tasks. |
Westmaas, J; Ayers, John; Dredze, Mark ; Althouse, Benjamin Evaluation of the Great American Smokeout by Digital Surveillance Inproceedings Society of Behavioral Medicine, 2015. BibTeX @inproceedings{Westmaas:2015rw,
title = {Evaluation of the Great American Smokeout by Digital Surveillance},
author = {J. Westmaas and John Ayers and Dredze, Mark and Benjamin Althouse},
year = {2015},
date = {2015-01-01},
booktitle = {Society of Behavioral Medicine},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Wang, Haoyu; Hovy, Eduard; Dredze, Mark The Hurricane Sandy Twitter Corpus Inproceedings AAAI Workshop on the World Wide Web and Public Health Intelligence, 2015. BibTeX @inproceedings{Wang:2015ve,
title = {The Hurricane Sandy Twitter Corpus},
author = {Haoyu Wang and Eduard Hovy and Dredze, Mark},
year = {2015},
date = {2015-01-01},
booktitle = {AAAI Workshop on the World Wide Web and Public Health Intelligence},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Yu, Mo; Dredze, Mark Learning Composition Models for Phrase Embeddings Journal Article pp. 227–242, 2015. Abstract | Links | BibTeX @article{TACL586,
title = {Learning Composition Models for Phrase Embeddings},
author = {Mo Yu and Dredze, Mark},
url = {https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/586/125},
year = {2015},
date = {2015-01-01},
pages = {227--242},
abstract = {Lexical embeddings can serve as useful representations for
words for a variety of NLP tasks, but learning embeddings for phrases can be
challenging. While separate embeddings are learned for each word, this is
infeasible for every phrase. We construct phrase embeddings by learning how
to compose word embeddings using features that capture phrase structure and
context. We propose efficient unsupervised and task-specific learning
objectives that scale our model to large datasets. We demonstrate
improvements on both language modeling and several phrase semantic
similarity tasks with various phrase lengths. We make the implementation of
our model and the datasets available for general use.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lexical embeddings can serve as useful representations for
words for a variety of NLP tasks, but learning embeddings for phrases can be
challenging. While separate embeddings are learned for each word, this is
infeasible for every phrase. We construct phrase embeddings by learning how
to compose word embeddings using features that capture phrase structure and
context. We propose efficient unsupervised and task-specific learning
objectives that scale our model to large datasets. We demonstrate
improvements on both language modeling and several phrase semantic
similarity tasks with various phrase lengths. We make the implementation of
our model and the datasets available for general use. |
Gormley, Matt ; Yu, Mo; Dredze, Mark Improved Relation Extraction with Feature-Rich Compositional Embedding Models Inproceedings Empirical Methods in Natural Language Processing (EMNLP), 2015. BibTeX @inproceedings{Gormley:2015ly,
title = {Improved Relation Extraction with Feature-Rich Compositional Embedding Models},
author = {Gormley, Matt and Mo Yu and Dredze, Mark},
year = {2015},
date = {2015-01-01},
booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Wolfe, Travis ; Dredze, Mark ; Van Durme, Benjamin Predicate Argument Alignment using a Global Coherence Model Inproceedings North American Chapter of the Association for Computational Linguistics (NAACL), 2015. Abstract | Links | BibTeX @inproceedings{Wolfe:2015qf,
title = {Predicate Argument Alignment using a Global Coherence Model},
author = {Wolfe, Travis and Dredze, Mark and Van Durme, Benjamin},
url = {http://aclweb.org/anthology/N/N15/N15-1002.pdf},
year = {2015},
date = {2015-01-01},
booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)},
abstract = {We present a joint model for predicate argument alignment. We leverage multiple sources of semantic information, including temporal ordering constraints between events. These are combined in a max-margin framework to find a globally consistent view of entities and events across multiple documents, which leads to improvements over a very strong local baseline.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a joint model for predicate argument alignment. We leverage multiple sources of semantic information, including temporal ordering constraints between events. These are combined in a max-margin framework to find a globally consistent view of entities and events across multiple documents, which leads to improvements over a very strong local baseline. |
Coppersmith, Glen ; Dredze, Mark ; Harman, Craig ; Hollingshead, Kristy; Mitchell, Margaret CLPsych 2015 Shared Task: Depression and PTSD on Twitter Inproceedings NAACL Workshop on Computational Linguistics and Clinical Psychology, 2015. Abstract | BibTeX @inproceedings{Coppersmith:2015eu,
title = {CLPsych 2015 Shared Task: Depression and PTSD on Twitter},
author = {Coppersmith, Glen and Dredze, Mark and Harman, Craig and Kristy Hollingshead and Mitchell, Margaret},
year = {2015},
date = {2015-01-01},
booktitle = {NAACL Workshop on Computational Linguistics and Clinical Psychology},
abstract = {This paper presents a summary of the Computational Linguistics and Clinical Psychology (CLPsych) 2015 shared and unshared tasks. These tasks aimed to provide apples-to-apples comparisons of various approaches to modeling language relevant to mental health from social media. The data used for these tasks is from Twitter users who state a diagnosis of depression or post traumatic stress disorder (PTSD) and demographically-matched community controls. The unshared task was a hackathon held at Johns Hopkins University in November 2014 to explore the data, and the shared task was conducted remotely, with each participating team submitted scores for a held-back test set of users. The shared task consisted of three binary classification experiments: (1) depression versus control, (2) PTSD versus control, and (3) depression versus PTSD. Classifiers were compared primarily via their average precision, though a number of other metrics are used along with this to allow a more nuanced interpretation of the performance measures.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a summary of the Computational Linguistics and Clinical Psychology (CLPsych) 2015 shared and unshared tasks. These tasks aimed to provide apples-to-apples comparisons of various approaches to modeling language relevant to mental health from social media. The data used for these tasks is from Twitter users who state a diagnosis of depression or post traumatic stress disorder (PTSD) and demographically-matched community controls. The unshared task was a hackathon held at Johns Hopkins University in November 2014 to explore the data, and the shared task was conducted remotely, with each participating team submitted scores for a held-back test set of users. The shared task consisted of three binary classification experiments: (1) depression versus control, (2) PTSD versus control, and (3) depression versus PTSD. Classifiers were compared primarily via their average precision, though a number of other metrics are used along with this to allow a more nuanced interpretation of the performance measures. |
Bushman, Brad; Newman, Katherine; Calvert, Sandra; Downey, Geraldine; Dredze, Mark ; Gottfredson, Michael; Jablonski, Nina; Masten, Ann; Morrill, Calvin; Neill, Daniel; Romer, Daniel; Webster, Daniel Youth Violence: What We Know and What We Need to Know Journal Article 2015. BibTeX @article{Bushman:2015fj,
title = {Youth Violence: What We Know and What We Need to Know},
author = {Brad Bushman and Katherine Newman and Sandra Calvert and Geraldine Downey and Dredze, Mark and Michael Gottfredson and Nina Jablonski and Ann Masten and Calvin Morrill and Daniel Neill and Daniel Romer and Daniel Webster},
year = {2015},
date = {2015-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
