@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@inproceedings{volkova-EtAl:2015:AAAI,

title = {Inferring Latent User Properties from Texts Published in Social Media (Demo)},

author = {Volkova, Svitlana and Yoram Bachrach and Michael Armstrong and Vijay Sharma},

url = {https://hltcoe.jhu.edu/wp-content/uploads/2016/11/17310_slides.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the Twenty-Ninth Conference on Artificial Intelligence (AAAI)},

address = {Austin, TX},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wintrode_Sell_EtAl_2015A,

title = {Content-Based Recommender Systems for Spoken Documents},

author = {Jonathan Wintrode and Gregory Sell and Aren Jansen and Michelle Fox and Daniel Garcia-Romero and Alan McCree},

url = {https://hltcoe.jhu.edu/wp-content/uploads/2016/10/Wintrode_Sell_EtAl_2015A.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing},

abstract = {Content-based recommender systems use preference ratings and features that characterize media to model users’ interests or information needs for making future recommendations. While previously developed in the music and text domains, we present an initial exploration of content-based recommendation for spoken documents using a corpus of public domain internet audio. Unlike familiar speech technologies of topic identification and spoken document retrieval, our recommendation task requires a more comprehensive notion of document relevance than bags-of-words would supply. Inspired by music recommender systems, we automatically extract a wide variety of content-based features to characterize non-linguistic aspects of the audio such as speaker, language, gender, and environment. To combine these heterogeneous information sources into a single relevance judgement, we evaluate feature, score, and hybrid fusion techniques. Our study provides an essential first exploration of the task and clearly demonstrates the value of a multisource approach over a bag-of-words baseline.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sell_Garcia-Romero_2015A,

title = {Diarization Resegmentation in the Factor Analysis Subspace},

author = {Gregory Sell and Daniel Garcia-Romero},

url = {https://hltcoe.jhu.edu/wp-content/uploads/2016/10/Sell_Garcia-Romero_2015A.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing},

abstract = {Resegmentation is an important post-processing step to refine the rough boundaries of diarization systems that rely on segment clustering of an initial uniform segmentation. Past work has primarily used a Viterbi resegmentation with MFCC features for this purpose. In this paper, we examine an algorithm for resegmentation that operates instead in factor analysis subspace. By combining this system with a speaker clustering front-end, we yield a diarization error rate of 11.5% on the CALLHOME conversational telephone speech corpus.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sell_Garcia-Romero_McCree_2015A,

title = {Speaker Diarization with I-Vectors from DNN Senone Posteriors},

author = {Gregory Sell and Daniel Garcia-Romero and Alan McCree},

url = {https://hltcoe.jhu.edu/wp-content/uploads/2016/10/Sell_Garcia-Romero_McCree_2015A.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of Interspeech},

abstract = {Motivated by recent gains in speaker identification by incorporating senone posteriors from deep neural networks (DNNs) into i-vector extraction, we examine similar enhancements to speaker diarization with i-vector clustering. We examine two DNNs with different numbers of senone targets in combination with a diagonal or full covariance universal background model (UBM) in the context of the multilingual corpus CALLHOME. Results show that the larger DNN with a full covariance UBM gives the best performance. The improvements appear to have a strong dependence on number of speakers in a conversation, and a lesser dependence on language. Overall, when combined with resegmentation, the proposed system improves CALLHOME performance to 10.3% DER.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wang:2015eu,

title = {Social Media as a Sensor of Air Quality and Public Response in China},

author = {Shiliang Wang and Michael Paul 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}

}

@inproceedings{Pavlick:2015bs,

title = {FrameNet+: Fast Paraphrastic Tripling of FrameNet},

author = {Ellie Pavlick and Wolfe, Travis and Pushpendre Rastogi and Callison-Burch, Chris and Dredze, Mark and Van Durme, Benjamin},

year  = {2015},

date = {2015-01-01},

booktitle = {Association for Computational Linguistics (ACL) (short paper)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Peng:2015rt,

title = {Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings},

author = {Nanyun Peng and Dredze, Mark},

year  = {2015},

date = {2015-01-01},

booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}