@inproceedings{8461893,

title = {End-to-End Multi-Speaker Speech Recognition},

author = {S Settle and J L Roux and T Hori and S Watanabe and J R Hershey},

doi = {10.1109/ICASSP.2018.8461893},

issn = {2379-190X},

year  = {2018},

date = {2018-04-01},

booktitle = {2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {4819-4823},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Knowles-Ortega-Koeh:2018:AMTA,

author = {Rebecca Knowles and John Ortega and Philipp} Comparison Machine Translation Paradigms Use Black-Box Fuzzy-Match Repair = {A title of for in Koeh},

url = {http://www.aclweb.org/anthology/W18-2108},

year  = {2018},

date = {2018-03-01},

booktitle = {Proceedings of the AMTA 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing},

pages = {249--255},

publisher = {Association for Machine Translation in the Americas},

address = {Boston, MA},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Knowles-Koehn:2018:AMTA,

author = {Rebecca Knowles and Philipp} {Lightweight Word-Level Confidence Estimation Neural Interactive Translation Prediction = title for Koehn},

url = {http://www.aclweb.org/anthology/W18-2102},

year  = {2018},

date = {2018-03-01},

booktitle = {Proceedings of the AMTA 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing},

pages = {35--40},

publisher = {Association for Machine Translation in the Americas},

address = {Boston, MA},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Marvin-Koehn:2018:AMTA,

author = {Rebecca Marvin and Philipp} {Exploring Word Sense Disambiguation Abilities Neural Machine Translation Systems (Non-archival Extended Abstract) = title of Koehn},

url = {http://www.aclweb.org/anthology/W18-1812},

year  = {2018},

date = {2018-03-01},

booktitle = {Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Papers)},

pages = {125--131},

publisher = {Association for Machine Translation in the Americas},

address = {Boston, MA},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{D18-1473,

title = {Hard Non-Monotonic Attention for Character-Level Transduction},

author = {Shijie Wu and Pamela Shapiro and Ryan Cotterell},

url = {http://aclweb.org/anthology/D18-1473},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},

pages = {4425--4438},

address = {Brussels, Belgium},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{W18-6011,

title = {Marrying Universal Dependencies and Universal Morphology},

author = {Arya D McCarthy and Miikka Silfverberg and Ryan Cotterell and Mans Hulden and David Yarowsky},

url = {http://aclweb.org/anthology/W18-6011},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)},

pages = {91--101},

address = {Brussels, Belgium},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Subramanian2018b,

title = {Student-Teacher Learning for BLSTM Mask-based Speech Enhancement},

author = {Aswin Shanmugam Subramanian and Szu-Jui Chen and Shinji Watanabe},

url = {http://dx.doi.org/10.21437/Interspeech.2018-2440},

doi = {10.21437/Interspeech.2018-2440},

year  = {2018},

date = {2018-01-01},

booktitle = {Proc. Interspeech 2018},

pages = {3249--3253},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sell2018d,

title = {Diarization is Hard: Some 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 Jesús Villalba and Matthew Maciejewski and Vimal Manohar and Najim Dehak and Daniel Povey and Shinji Watanabe and Sanjeev Khudanpur},

url = {http://dx.doi.org/10.21437/Interspeech.2018-1893},

doi = {10.21437/Interspeech.2018-1893},

year  = {2018},

date = {2018-01-01},

booktitle = {Proc. Interspeech 2018},

pages = {2808--2812},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Delcroix2018,

title = {Auxiliary Feature Based Adaptation of End-to-end ASR Systems},

author = {Marc Delcroix and Shinji Watanabe and Atsunori Ogawa and Shigeki Karita and Tomohiro Nakatani},

url = {http://dx.doi.org/10.21437/Interspeech.2018-1438},

doi = {10.21437/Interspeech.2018-1438},

year  = {2018},

date = {2018-01-01},

booktitle = {Proc. Interspeech 2018},

pages = {2444--2448},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Renduchintala2018,

title = {Multi-Modal Data Augmentation for End-to-end ASR},

author = {Adithya Renduchintala and Shuoyang Ding and Matthew Wiesner and Shinji Watanabe},

url = {http://dx.doi.org/10.21437/Interspeech.2018-2456},

doi = {10.21437/Interspeech.2018-2456},

year  = {2018},

date = {2018-01-01},

booktitle = {Proc. Interspeech 2018},

pages = {2394--2398},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Watanabe2018,

title = {ESPnet: End-to-End Speech Processing Toolkit},

author = {Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},

url = {http://dx.doi.org/10.21437/Interspeech.2018-1456},

doi = {10.21437/Interspeech.2018-1456},

year  = {2018},

date = {2018-01-01},

booktitle = {Proc. Interspeech 2018},

pages = {2207--2211},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Frederiksen2018,

title = {Effectiveness of Single-Channel BLSTM Enhancement for Language Identification},

author = {Peter Sibbern Frederiksen and Jesús Villalba and Shinji Watanabe and Zheng-Hua Tan and Najim Dehak},

url = {http://dx.doi.org/10.21437/Interspeech.2018-2458},

doi = {10.21437/Interspeech.2018-2458},

year  = {2018},

date = {2018-01-01},

booktitle = {Proc. Interspeech 2018},

pages = {1823--1827},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chen2018b,

title = {Building State-of-the-art Distant Speech Recognition Using the CHiME-4 Challenge with a Setup of Speech Enhancement Baseline},

author = {Szu-Jui Chen and Aswin Shanmugam Subramanian and Hainan Xu and Shinji Watanabe},

url = {http://dx.doi.org/10.21437/Interspeech.2018-1262},

doi = {10.21437/Interspeech.2018-1262},

year  = {2018},

date = {2018-01-01},

booktitle = {Proc. Interspeech 2018},

pages = {1571--1575},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Barker2018,

title = {The Fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, Task and Baselines},

author = {Jon Barker and Shinji Watanabe and Emmanuel Vincent and Jan Trmal},

url = {http://dx.doi.org/10.21437/Interspeech.2018-1768},

doi = {10.21437/Interspeech.2018-1768},

year  = {2018},

date = {2018-01-01},

booktitle = {Proc. Interspeech 2018},

pages = {1561--1565},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Hayashi2018,

title = {Multi-Head Decoder for End-to-End Speech Recognition},

author = {Tomoki Hayashi and Shinji Watanabe and Tomoki Toda and Kazuya Takeda},

url = {http://dx.doi.org/10.21437/Interspeech.2018-1655},

doi = {10.21437/Interspeech.2018-1655},

year  = {2018},

date = {2018-01-01},

booktitle = {Proc. Interspeech 2018},

pages = {801--805},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Karita2018,

title = {Semi-Supervised End-to-End Speech Recognition},

author = {Shigeki Karita and Shinji Watanabe and Tomoharu Iwata and Atsunori Ogawa and Marc Delcroix},

url = {http://dx.doi.org/10.21437/Interspeech.2018-1746},

doi = {10.21437/Interspeech.2018-1746},

year  = {2018},

date = {2018-01-01},

booktitle = {Proc. Interspeech 2018},

pages = {2--6},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{P18-1244,

author = {Hiroshi Seki and Takaaki Hori and Shinji Watanabe and Jonathan Le Roux and John R Hershey},

url = {http://aclweb.org/anthology/P18-1244},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},

pages = {2620--2630},

address = {Melbourne, Australia},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wood2018challenges,

title = {Challenges of Using Text Classifiers for Causal Inference},

author = {Zach Wood-Doughty and Ilya Shpitser and Mark Dredze},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},

pages = {4586--4598},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wood-Doughty:2018qe,

title = {Challenges of Using Text Classifiers for Causal Inference},

author = {Zachary Wood-Doughty and Ilya Shpitser and Mark Dredze},

year  = {2018},

date = {2018-01-01},

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

abstract = {Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets. While text classifiers produce low-dimensional outputs, their use in causal inference has not previously been studied. To facilitate causal analyses based on language data, we consider the role that text classifiers can play in causal inference through established modeling mechanisms from the causality literature on missing data and measurement error. We demonstrate how to conduct causal analyses using text classifiers on simulated and Yelp data, and discuss the opportunities and challenges of future work that uses text data in causal inference.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sekara:2018uo,

title = {Trends in the Adoption of Corporate Child Labor Policies: An Analysis with Bloomberg Terminal ESG Data},

author = {Vedran Sekara and Alex Rutherford and Gideon Mann and Mark Dredze and Natalia Adler and Manuel Garc 'i},

year  = {2018},

date = {2018-01-01},

booktitle = {Bloomberg Data for Good Exchange},

abstract = {Over 150 million children worldwide are estimated to be engaged in some form of child labor, with nearly one in every four children between the ages of 5 and 14 engaged in potentially harmful work in the world's poorest countries. Child labor compromises children's physical, mental, social and educational development. It also reinforces cycles of poverty, negatively affecting the ecosystem necessary for business to thrive in a sustainable manner. Against a backdrop of multiple international and national laws against child labor, corporations also adopt policies on child labor. However, new methods of globally dispersed production have made this commitment to sustainability issues across supply chains more challenging. In this work we examine, through the lens of Bloomberg's environmental, social and governance (ESG) and financial data, trends in corporate child labor policies and their relationship to classic economic variables as a first step in understanding sustainability issues across global supply networks.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wood-Doughty:2018qd,

title = {Convolutions Are All You Need (For Classifying Character Sequences)},

author = {Zachary Wood-Doughty and Nicholas Andrews and Mark Dredze},

year  = {2018},

date = {2018-01-01},

booktitle = {EMNLP Workshop on Noisy User-generated Text (W-NUT)},

abstract = {While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences. When text is modeled as characters instead of words, the longer sequences make RNNs a poor choice. Convolutional neural networks (CNNs), although somewhat less ubiquitous than RNNs, have an internal structure more appropriate for long-distance character dependencies. To better understand how CNNs and RNNs differ in handling long sequences, we use them for text classification tasks in several character-level social media datasets. The CNN models vastly outperform the RNN models in our experiments, suggesting that CNNs are superior to RNNs at learning to classify character-level data.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Benton:2018dk,

title = {Using Author Embeddings to Improve Tweet Stance Classification},

author = {Adrian Benton and Mark Dredze},

year  = {2018},

date = {2018-01-01},

booktitle = {EMNLP Workshop on Noisy User-generated Text (W-NUT)},

abstract = {Many social media classification tasks analyze the content of a message, but do not consider the context of the message. For example, in tweet stance classification -- where a tweet is categorized according to a view-point it espouses -- the expressed viewpoint depends on latent beliefs held by the user. In this paper we investigate whether incorporating knowledge about the author can improve tweet stance classification. Furthermore, since author information and embeddings are often unavailable for labeled training examples, we propose a semi-supervised pre-training method to predict user embeddings. Although the neural stance classifiers we learn are often outperformed by a baseline SVM, author embedding pre-training yields improvements over a non-pre-trained neural network on four out of five domains in the SemEval 2016 6A tweet stance classification task. In a tweet gun control stance classification dataset, improvements from pre-training are only apparent when training data is limited.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Rouhizadeh:2018fq,

title = {Identifying Social Determinants of Health from Clinical Notes: A Rule-Based Approach},

author = {Masoud Rouhizadeh and Elham Hatef and Mark Dredze and Christopher Chute and Hadi Kharrazi},

year  = {2018},

date = {2018-01-01},

booktitle = {AMIA Natural Language Processing Working Group Pre-Symposium},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{moro2018study,

title = {Study of the automatic detection of Parkison's Disease based on speaker recognition technologies and allophonic distillation},

author = {J-A Moro-Velázquez Laureano Gómez-Garc{'i}a and Juan Ignacio Godino-Llorente and Jan Rusz and Sabine Skodda and J R Orozco Arroyave and Elmar Noth and Najim Dehak},

year  = {2018},

date = {2018-01-01},

booktitle = {Engineering in Medicine and Biology Society (EMBC),2018 40th Annual International Conference of the IEEE.},

pages = {1404--1407},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cotterell-et-al-2018-scil,

title = {Quantifying the Trade-off Between Two Types of 

 Morphological Complexity},

author = {Ryan Cotterell and Christo Kirov and Mans Hulden and Jason Eisner},

url = {http://cs.jhu.edu/~jason/papers/#cotterell-et-al-2018-scil},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings of the Society for Computation in 

 Linguistics (SCiL)},

volume = {1},

number = {30},

address = {Salt Lake City},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wang-eisner-2018-scil,

title = {Predicting Fine-Grained Syntactic Typology from 

 Surface Features},

author = {Dingquan Wang and Jason Eisner},

url = {http://cs.jhu.edu/~jason/papers/#wang-eisner-2018-scil},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings of the Society for Computation in 

 Linguistics (SCiL)},

volume = {1},

number = {39},

address = {Salt Lake City},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{filardo-eisner-2018-sysml,

title = {Treating Machine Learning Algorithms as Declaratively 

 Specified Circuits},

author = {Jason Eisner and Nathaniel Wesley Filardo},

url = {http://cs.jhu.edu/~jason/papers/#filardo-eisner-2018-sysml},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings of the SysML Conference},

address = {Palo Alto},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wang-eisner-2018-emnlp,

title = {Synthetic Data Made to Order: The Case of Parsing},

author = {Dingquan Wang and Jason Eisner},

url = {http://cs.jhu.edu/~jason/papers/#wang-eisner-2018-emnlp},

year  = {2018},

date = {2018-01-01},

booktitle = {Proceedings of the Conference on Empirical Methods in 

 Natural Language Processing (EMNLP)},

address = {Brussels},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mccree2018language,

title = {Language Recognition for Telephone and Video Speech: The JHU HLTCOE Submission for NIST LRE17},

author = {Alan Mccree and David Snyder and Gregory Sell and Daniel Garcia-Romero},

year  = {2018},

date = {2018-01-01},

booktitle = {Proc. Odyssey 2018 The Speaker and Language Recognition Workshop},

pages = {68--73},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sell2018audio,

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},

year  = {2018},

date = {2018-01-01},

booktitle = {2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {3031--3035},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Zhu2018b,

title = {Self-Attentive Speaker Embeddings for Text-Independent Speaker Verification},

author = {Yingke Zhu and Tom Ko and David Snyder and Brian Mak and Daniel Povey},

url = {http://www.danielpovey.com/files/2018_interspeech_xvector_attention.pdf},

doi = {10.21437/Interspeech.2018-1158},

year  = {2018},

date = {2018-01-01},

booktitle = {Proc. Interspeech 2018},

pages = {3573--3577},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Silnova2018,

title = {Fast Variational Bayes for Heavy-tailed PLDA Applied to i-vectors and x-vectors},

author = {Anna Silnova and Niko Brümmer and Daniel Garcia-Romero and David Snyder and Lukáš Burget},

url = {http://dx.doi.org/10.21437/Interspeech.2018-2128},

doi = {10.21437/Interspeech.2018-2128},

year  = {2018},

date = {2018-01-01},

booktitle = {Proc. Interspeech 2018},

pages = {72--76},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sell2018b,

title = {Diarization is Hard: Some 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 Jesús Villalba and Matthew Maciejewski and Vimal Manohar and Najim Dehak and Daniel Povey and Shinji Watanabe and Sanjeev Khudanpur},

url = {http://www.danielpovey.com/files/2018_interspeech_dihard.pdf},

doi = {10.21437/Interspeech.2018-1893},

year  = {2018},

date = {2018-01-01},

booktitle = {Proc. Interspeech 2018},

pages = {2808--2812},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{snyder2018spokenb,

title = {Spoken language recognition using x-vectors},

author = {David Snyder and Daniel Garcia-Romero and Alan McCree and Gregory Sell and Daniel Povey and Sanjeev Khudanpur},

url = {http://www.danielpovey.com/files/2018_odyssey_xvector_lid.pdf},

year  = {2018},

date = {2018-01-01},

booktitle = {Odyssey: The Speaker and Language Recognition Workshop, Les Sables d’Olonne},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{snyder2018xvector,

title = {X-vectors: Robust DNN Embeddings for Speaker Recognition},

author = {D Snyder and D Garcia-Romero and G Sell and D Povey and S Khudanpur},

url = {http://www.danielpovey.com/files/2018_icassp_xvectors.pdf},

year  = {2018},

date = {2018-01-01},

booktitle = {2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Subramanian2018,

title = {Student-Teacher Learning for BLSTM Mask-based Speech Enhancement},

author = {Aswin Shanmugam Subramanian and Szu-Jui Chen and Shinji Watanabe},

url = {http://dx.doi.org/10.21437/Interspeech.2018-2440},

doi = {10.21437/Interspeech.2018-2440},

year  = {2018},

date = {2018-01-01},

booktitle = {Proc. Interspeech 2018},

pages = {3249--3253},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{snyder2018spoken,

title = {Spoken language recognition using x-vectors},

author = {David Snyder and Daniel Garcia-Romero and Alan McCree and Gregory Sell and Daniel Povey and Sanjeev Khudanpur},

year  = {2018},

date = {2018-01-01},

booktitle = {Odyssey: The Speaker and Language Recognition Workshop, Les Sables d’Olonne},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{manohar2018semi,

title = {Semi-supervised training of acoustic models using lattice-free MMI},

author = {Vimal Manohar and Hossein Hadian and Daniel Povey and Sanjeev Khudanpur},

year  = {2018},

date = {2018-01-01},

booktitle = {2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

pages = {4844--4848},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{wang17multitask,

title = {A Multi-task Learning Approach to Adapting Bilingual Word Embeddings for Cross-lingual Named Entity Recognition},

author = {Dingquan Wang and Nanyun Peng and Kevin Duh},

url = {http://www.aclweb.org/anthology/I17-2065},

year  = {2017},

date = {2017-11-01},

booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},

pages = {383--388},

publisher = {Asian Federation of Natural Language Processing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cotterell17ner,

title = {Low-Resource Named Entity Recognition with Cross-lingual, Character-Level Neural Conditional Random Fields},

author = {Ryan Cotterell and Kevin Duh},

url = {http://www.aclweb.org/anthology/I17-2016},

year  = {2017},

date = {2017-11-01},

booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},

pages = {91--96},

publisher = {Asian Federation of Natural Language Processing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{khayrallah17adapt,

title = {Neural Lattice Search for Domain Adaptation in Machine Translation},

author = {Huda Khayrallah and Gaurav Kumar and Kevin Duh and Matt Post and Philipp Koehn},

url = {http://www.aclweb.org/anthology/I17-2004},

year  = {2017},

date = {2017-11-01},

booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},

pages = {20--25},

publisher = {Asian Federation of Natural Language Processing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{white17inference,

title = {Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework},

author = {Aaron Steven White and Pushpendre Rastogi and Kevin Duh and Benjamin Van Durme},

url = {http://www.aclweb.org/anthology/I17-1100},

year  = {2017},

date = {2017-11-01},

booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},

pages = {996--1005},

publisher = {Asian Federation of Natural Language Processing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{shen17reasoning,

title = {An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks},

author = {Yelong Shen and Xiaodong Liu and Kevin Duh and Jianfeng Gao},

url = {http://www.aclweb.org/anthology/I17-1096},

year  = {2017},

date = {2017-11-01},

booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},

pages = {957--966},

publisher = {Asian Federation of Natural Language Processing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{zhang17selective,

title = {Selective Decoding for Cross-lingual Open Information Extraction},

author = {Sheng Zhang and Kevin Duh and Benjamin Van Durme},

url = {http://www.aclweb.org/anthology/I17-1084},

year  = {2017},

date = {2017-11-01},

booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},

pages = {832--842},

publisher = {Asian Federation of Natural Language Processing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ding-EtAl:2017:WMT,

title = {The JHU Machine Translation Systems for WMT 2017},

author = {Shuoyang Ding and Huda Khayrallah and Philipp Koehn and Matt Post and Gaurav Kumar and Kevin Duh},

url = {http://www.aclweb.org/anthology/W17-4724},

year  = {2017},

date = {2017-09-01},

booktitle = {Proceedings of the Second Conference on Machine Translation},

pages = {276--282},

publisher = {Association for Computational Linguistics},

address = {Copenhagen, Denmark},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nadejde-EtAl:2017:WMT,

title = {Predicting Target Language CCG Supertags Improves Neural Machine Translation},

author = {Maria Nadejde and Siva Reddy and Rico Sennrich and Tomasz Dwojak and Marcin Junczys-Dowmunt and Philipp Koehn and Alexandra Birch},

url = {http://www.aclweb.org/anthology/W17-4707},

year  = {2017},

date = {2017-09-01},

booktitle = {Proceedings of the Second Conference on Machine Translation},

pages = {68--79},

publisher = {Association for Computational Linguistics},

address = {Copenhagen, Denmark},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bojar-EtAl:2017:WMT1,

title = {Findings of the 2017 Conference on Machine Translation (WMT17)},

author = {Ondřej Bojar and Rajen Chatterjee and Christian Federmann and Yvette Graham and Barry Haddow and Shujian Huang and Matthias Huck and Philipp Koehn and Qun Liu and Varvara Logacheva and Christof Monz and Matteo Negri and Matt Post and Raphael Rubino and Lucia Specia and Marco Turchi},

url = {http://www.aclweb.org/anthology/W17-4717},

year  = {2017},

date = {2017-09-01},

booktitle = {Proceedings of the Second Conference on Machine Translation},

pages = {169--214},

publisher = {Association for Computational Linguistics},

address = {Copenhagen, Denmark},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{xu-koehn:2017:EMNLP2017,

title = {Zipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora},

author = {Hainan Xu and Philipp Koehn},

url = {https://www.aclweb.org/anthology/D17-1318},

year  = {2017},

date = {2017-09-01},

booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},

pages = {2935--2940},

publisher = {Association for Computational Linguistics},

address = {Copenhagen, Denmark},

abstract = {We introduce Zipporah, a fast and scalable data cleaning system. We propose a 

	novel type of bag-of-words translation feature, and train logistic regression 

	models to classify good data and synthetic noisy data in the proposed feature 

	space. The trained model is used to score parallel sentences in the data pool 

	for selection. As shown in experiments, Zipporah selects a high-quality 

	parallel corpus from a large, mixed quality data pool. In particular, for one 

	noisy dataset, Zipporah achieves a 2.1 BLEU score improvement with using 1/5 of 

	the data over using the entire corpus.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{renduchintala-koehn-eisner:2017:CoNLL,

title = {Knowledge Tracing in Sequential Learning of Inflected Vocabulary},

author = {Adithya Renduchintala and Philipp Koehn and Jason Eisner},

url = {http://aclweb.org/anthology/K17-1025},

year  = {2017},

date = {2017-08-01},

booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},

pages = {238--247},

publisher = {Association for Computational Linguistics},

address = {Vancouver, Canada},

abstract = {We present a feature-rich knowledge tracing method that captures a student's 

	acquisition and retention of knowledge during a foreign language phrase 

	learning task. We model the student's behavior as making predictions under a 

	log-linear model, and adopt a neural gating mechanism to model how the student 

	updates their log-linear parameters in response to feedback. The gating 

	mechanism allows the model to learn complex patterns of retention and 

	acquisition for each feature, while the log-linear parameterization results in 

	an interpretable knowledge state. We collect human data and evaluate several 

	versions of the model.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{koehn-knowles:2017:NMT,

title = {Six Challenges for Neural Machine Translation},

author = {Philipp Koehn and Rebecca Knowles},

url = {http://www.aclweb.org/anthology/W17-3204},

year  = {2017},

date = {2017-08-01},

booktitle = {Proceedings of the First Workshop on Neural Machine Translation},

pages = {28--39},

publisher = {Association for Computational Linguistics},

address = {Vancouver},

abstract = {We explore six challenges for neural machine translation: domain mismatch, 

	amount of training data, rare words, long sentences, word alignment, and beam 

	search. We show both deficiencies and improvements over the quality of 

	phrase-based statistical machine translation.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sakaguchi-post-vandurme:2017:Short,

title = {Error-repair Dependency Parsing for Ungrammatical Texts},

author = {Keisuke Sakaguchi and Matt Post and Benjamin Van Durme},

url = {http://aclweb.org/anthology/P17-2030},

year  = {2017},

date = {2017-07-01},

booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},

pages = {189--195},

publisher = {Association for Computational Linguistics},

address = {Vancouver, Canada},

abstract = {We propose a new dependency parsing scheme which jointly parses a sentence and 

	repairs grammatical errors by extending the non-directional transition-based 

	formalism of Goldberg and Elhadad (2010) with three additional actions: 

	SUBSTITUTE, DELETE, INSERT. Because these actions may cause an infinite loop in 

	derivation, we also introduce simple constraints that ensure the parser 

	termination. We evaluate our model with respect to dependency accuracy and 

	grammaticality improvements for ungrammatical sentences, demonstrating the 

	robustness and applicability of our scheme.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}