1345 entries « ‹ 2 of 68
› » 21.
Settle, S; Roux, J L; Hori, T; Watanabe, S; Hershey, J R
End-to-End Multi-Speaker Speech Recognition Inproceedings
In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4819-4823, 2018, ISSN: 2379-190X.
@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}
}
22.
Knowles, Rebecca; Ortega, John; title of for in Koeh, Philipp Comparison Machine Translation Paradigms Use Black-Box Fuzzy-Match Repair = A
Inproceedings
In: Proceedings of the AMTA 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing, pp. 249–255, Association for Machine Translation in the Americas, Boston, MA, 2018.
@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}
}
23.
Knowles, Rebecca; title for Koehn, Philipp Lightweight Word-Level Confidence Estimation Neural Interactive Translation Prediction =
Inproceedings
In: Proceedings of the AMTA 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing, pp. 35–40, Association for Machine Translation in the Americas, Boston, MA, 2018.
@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}
}
24.
Marvin, Rebecca; title of Koehn, Philipp Exploring Word Sense Disambiguation Abilities Neural Machine Translation Systems (Non-archival Extended Abstract) =
Inproceedings
In: Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Papers), pp. 125–131, Association for Machine Translation in the Americas, Boston, MA, 2018.
@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}
}
25.
Wu, Shijie; Shapiro, Pamela; Cotterell, Ryan
Hard Non-Monotonic Attention for Character-Level Transduction Inproceedings
In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4425–4438, Brussels, Belgium, 2018.
@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}
}
26.
McCarthy, Arya D; Silfverberg, Miikka; Cotterell, Ryan; Hulden, Mans; Yarowsky, David
Marrying Universal Dependencies and Universal Morphology Inproceedings
In: Proceedings of the Second Workshop on Universal Dependencies (UDW 2018), pp. 91–101, Brussels, Belgium, 2018.
@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}
}
27.
Subramanian, Aswin Shanmugam; Chen, Szu-Jui; Watanabe, Shinji
Student-Teacher Learning for BLSTM Mask-based Speech Enhancement Inproceedings
In: Proc. Interspeech 2018, pp. 3249–3253, 2018.
@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}
}
28.
Sell, Gregory; Snyder, David; McCree, Alan; Garcia-Romero, Daniel; Villalba, Jesús; Maciejewski, Matthew; Manohar, Vimal; Dehak, Najim; Povey, Daniel; Watanabe, Shinji; Khudanpur, Sanjeev
Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge Inproceedings
In: Proc. Interspeech 2018, pp. 2808–2812, 2018.
@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}
}
29.
Delcroix, Marc; Watanabe, Shinji; Ogawa, Atsunori; Karita, Shigeki; Nakatani, Tomohiro
Auxiliary Feature Based Adaptation of End-to-end ASR Systems Inproceedings
In: Proc. Interspeech 2018, pp. 2444–2448, 2018.
@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}
}
30.
Renduchintala, Adithya; Ding, Shuoyang; Wiesner, Matthew; Watanabe, Shinji
Multi-Modal Data Augmentation for End-to-end ASR Inproceedings
In: Proc. Interspeech 2018, pp. 2394–2398, 2018.
@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}
}
31.
Watanabe, Shinji; Hori, Takaaki; Karita, Shigeki; Hayashi, Tomoki; Nishitoba, Jiro; Unno, Yuya; Soplin, Nelson Enrique Yalta; Heymann, Jahn; Wiesner, Matthew; Chen, Nanxin; Renduchintala, Adithya; Ochiai, Tsubasa
ESPnet: End-to-End Speech Processing Toolkit Inproceedings
In: Proc. Interspeech 2018, pp. 2207–2211, 2018.
@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}
}
32.
Frederiksen, Peter Sibbern; Villalba, Jesús; Watanabe, Shinji; Tan, Zheng-Hua; Dehak, Najim
Effectiveness of Single-Channel BLSTM Enhancement for Language Identification Inproceedings
In: Proc. Interspeech 2018, pp. 1823–1827, 2018.
@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}
}
33.
Chen, Szu-Jui; Subramanian, Aswin Shanmugam; Xu, Hainan; Watanabe, Shinji
Building State-of-the-art Distant Speech Recognition Using the CHiME-4 Challenge with a Setup of Speech Enhancement Baseline Inproceedings
In: Proc. Interspeech 2018, pp. 1571–1575, 2018.
@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}
}
34.
Barker, Jon; Watanabe, Shinji; Vincent, Emmanuel; Trmal, Jan
The Fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, Task and Baselines Inproceedings
In: Proc. Interspeech 2018, pp. 1561–1565, 2018.
@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}
}
35.
Hayashi, Tomoki; Watanabe, Shinji; Toda, Tomoki; Takeda, Kazuya
Multi-Head Decoder for End-to-End Speech Recognition Inproceedings
In: Proc. Interspeech 2018, pp. 801–805, 2018.
@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}
}
36.
Karita, Shigeki; Watanabe, Shinji; Iwata, Tomoharu; Ogawa, Atsunori; Delcroix, Marc
Semi-Supervised End-to-End Speech Recognition Inproceedings
In: Proc. Interspeech 2018, pp. 2–6, 2018.
@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}
}
37.
Seki, Hiroshi; Hori, Takaaki; Watanabe, Shinji; Roux, Jonathan Le; Hershey, John R
Inproceedings
In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2620–2630, Melbourne, Australia, 2018.
@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}
}
38.
Watanabe, Shinji; Virtanen, Tuomas; Kolossa, Dorothea
Application of Source Separation to Robust Speech Analysis and Recognition Book Chapter
In: Audio Source Separation and Speech Enhancement, Chapter 17, pp. 377-411, Wiley-Blackwell, 2018, ISBN: 9781119279860.
@inbook{doi:10.1002/9781119279860.ch17,
title = {Application of Source Separation to Robust Speech Analysis and Recognition},
author = {Shinji Watanabe and Tuomas Virtanen and Dorothea Kolossa},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119279860.ch17},
doi = {10.1002/9781119279860.ch17},
isbn = {9781119279860},
year = {2018},
date = {2018-01-01},
booktitle = {Audio Source Separation and Speech Enhancement},
pages = {377-411},
publisher = {Wiley-Blackwell},
chapter = {17},
abstract = {Summary This chapter describes applications of source separation techniques to robust speech analysis and recognition, including automatic speech recognition, speaker/language identification, emotion and paralinguistic analysis, and audiovisual analysis. These are the most successful applications in audio and speech processing with various commercial products. However, the robustness against noise or non-target speech still remains a challenging issue, and source separation and speech enhancement techniques are gathering large attention in the community. This chapter systematically describes how source separation and speech enhancement techniques are applied to improve the robustness of these applications.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Summary This chapter describes applications of source separation techniques to robust speech analysis and recognition, including automatic speech recognition, speaker/language identification, emotion and paralinguistic analysis, and audiovisual analysis. These are the most successful applications in audio and speech processing with various commercial products. However, the robustness against noise or non-target speech still remains a challenging issue, and source separation and speech enhancement techniques are gathering large attention in the community. This chapter systematically describes how source separation and speech enhancement techniques are applied to improve the robustness of these applications.
39.
Wood-Doughty, Zach; Shpitser, Ilya; Dredze, Mark
Challenges of Using Text Classifiers for Causal Inference Inproceedings
In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4586–4598, 2018.
@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}
}
40.
Chen, Tao; Dredze, Mark
Vaccine Images on Twitter: What is Shared and Why Journal Article
In: Journal of Medical Internet Research (JMIR), 2018.
@article{Chen:2018tg,
title = {Vaccine Images on Twitter: What is Shared and Why},
author = {Tao Chen and Mark Dredze},
year = {2018},
date = {2018-01-01},
journal = {Journal of Medical Internet Research (JMIR)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
1345 entries « ‹ 2 of 68
› » 1345 entries « ‹ 2 of 27
› » 2018
Ayers, John W; Caputi, Theodore L; Nebeker, Camille; Dredze, Mark
Don't quote me: reverse identification of research participants in social media studies Journal Article
In: Nature Digital Medicine, vol. 1, no. 30, 2018.
@article{Ayers:2018eb,
title = {Don't quote me: reverse identification of research participants in social media studies},
author = {John W Ayers and Theodore L Caputi and Camille Nebeker and Mark Dredze},
year = {2018},
date = {2018-01-01},
journal = {Nature Digital Medicine},
volume = {1},
number = {30},
abstract = {We investigated if participants in social media surveillance studies could be reverse identified by reviewing all articles published on PubMed in 2015 or 2016 with the words ``Twitter'' and either ``read,'' ``coded,'' or ``content'' in the title or abstract. Seventy-two percent (95% CI: 63--80) of articles quoted at least one participant's tweet and searching for the quoted content led to the participant 84% (95% CI: 74--91) of the time. Twenty-one percent (95% CI: 13--29) of articles disclosed a participant's Twitter username thereby making the participant immediately identifiable. Only one article reported obtaining consent to disclose identifying information and institutional review board (IRB) involvement was mentioned in only 40% (95% CI: 31--50) of articles, of which 17% (95% CI: 10--25) received IRB-approval and 23% (95% CI:16--32) were deemed exempt. Biomedical publications are routinely including identifiable information by quoting tweets or revealing usernames which, in turn, violates ICMJE ethical standards governing scientific ethics, even though said content is scientifically unnecessary. We propose that authors convey aggregate findings without revealing participants' identities, editors refuse to publish reports that reveal a participant's identity, and IRBs attend to these privacy issues when reviewing studies involving social media data. These strategies together will ensure participants are protected going forward.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We investigated if participants in social media surveillance studies could be reverse identified by reviewing all articles published on PubMed in 2015 or 2016 with the words ``Twitter'' and either ``read,'' ``coded,'' or ``content'' in the title or abstract. Seventy-two percent (95% CI: 63--80) of articles quoted at least one participant's tweet and searching for the quoted content led to the participant 84% (95% CI: 74--91) of the time. Twenty-one percent (95% CI: 13--29) of articles disclosed a participant's Twitter username thereby making the participant immediately identifiable. Only one article reported obtaining consent to disclose identifying information and institutional review board (IRB) involvement was mentioned in only 40% (95% CI: 31--50) of articles, of which 17% (95% CI: 10--25) received IRB-approval and 23% (95% CI:16--32) were deemed exempt. Biomedical publications are routinely including identifiable information by quoting tweets or revealing usernames which, in turn, violates ICMJE ethical standards governing scientific ethics, even though said content is scientifically unnecessary. We propose that authors convey aggregate findings without revealing participants' identities, editors refuse to publish reports that reveal a participant's identity, and IRBs attend to these privacy issues when reviewing studies involving social media data. These strategies together will ensure participants are protected going forward.
Wood-Doughty, Zachary; Shpitser, Ilya; Dredze, Mark
Challenges of Using Text Classifiers for Causal Inference Inproceedings
In: Empirical Methods in Natural Language Processing (EMNLP), 2018.
@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}
}
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.
Sekara, Vedran; Rutherford, Alex; Mann, Gideon; Dredze, Mark; Adler, Natalia; 'i, Manuel Garc
Trends in the Adoption of Corporate Child Labor Policies: An Analysis with Bloomberg Terminal ESG Data Inproceedings
In: Bloomberg Data for Good Exchange, 2018.
@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}
}
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.
Wood-Doughty, Zachary; Andrews, Nicholas; Dredze, Mark
Convolutions Are All You Need (For Classifying Character Sequences) Inproceedings
In: EMNLP Workshop on Noisy User-generated Text (W-NUT), 2018.
@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}
}
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.
Benton, Adrian; Dredze, Mark
Using Author Embeddings to Improve Tweet Stance Classification Inproceedings
In: EMNLP Workshop on Noisy User-generated Text (W-NUT), 2018.
@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}
}
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.
Broniatowski, David A; Jamison, Amelia M; Qi, SiHua; AlKulaib, Lulwah; Chen, Tao; Benton, Adrian; Quinn, Sandra C; Dredze, Mark
Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate Journal Article
In: American Journal of Public Health (AJPH), 2018.
@article{broniatowski:2018a,
title = {Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate},
author = {David A Broniatowski and Amelia M Jamison and SiHua Qi and Lulwah AlKulaib and Tao Chen and Adrian Benton and Sandra C Quinn and Mark Dredze},
year = {2018},
date = {2018-01-01},
journal = {American Journal of Public Health (AJPH)},
abstract = {Objectives. To understand how Twitter bots and trolls (``bots'') promote online health content.
Methods. We compared bots' to average users' rates of vaccine-relevant messages, which we collected online from July 2014 through September 2017. We estimated the likelihood that users were bots, comparing proportions of polarized and antivaccine tweets across user types. We conducted a content analysis of a Twitter hashtag associated with Russian troll activity.
Results. Compared with average users, Russian trolls (χ2(1) = 102.0; P < .001), sophisticated bots (χ2(1) = 28.6; P < .001), and ``content polluters'' (χ2(1) = 7.0; P < .001) tweeted about vaccination at higher rates. Whereas content polluters posted more antivaccine content (χ2(1) = 11.18; P < .001), Russian trolls amplified both sides. Unidentifiable accounts were more polarized (χ2(1) = 12.1; P < .001) and antivaccine (χ2(1) = 35.9; P < .001). Analysis of the Russian troll hashtag showed that its messages were more political and divisive.
Conclusions. Whereas bots that spread malware and unsolicited content disseminated antivaccine messages, Russian trolls promoted discord. Accounts masquerading as legitimate users create false equivalency, eroding public consensus on vaccination.
Public Health Implications. Directly confronting vaccine skeptics enables bots to legitimize the vaccine debate. More research is needed to determine how best to combat bot-driven content. (Am J Public Health. Published online ahead of print August 23, 2018: e1--e7. doi:10.2105/AJPH.2018.304567)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objectives. To understand how Twitter bots and trolls (``bots'') promote online health content.
Methods. We compared bots' to average users' rates of vaccine-relevant messages, which we collected online from July 2014 through September 2017. We estimated the likelihood that users were bots, comparing proportions of polarized and antivaccine tweets across user types. We conducted a content analysis of a Twitter hashtag associated with Russian troll activity.
Results. Compared with average users, Russian trolls (χ2(1) = 102.0; P < .001), sophisticated bots (χ2(1) = 28.6; P < .001), and ``content polluters'' (χ2(1) = 7.0; P < .001) tweeted about vaccination at higher rates. Whereas content polluters posted more antivaccine content (χ2(1) = 11.18; P < .001), Russian trolls amplified both sides. Unidentifiable accounts were more polarized (χ2(1) = 12.1; P < .001) and antivaccine (χ2(1) = 35.9; P < .001). Analysis of the Russian troll hashtag showed that its messages were more political and divisive.
Conclusions. Whereas bots that spread malware and unsolicited content disseminated antivaccine messages, Russian trolls promoted discord. Accounts masquerading as legitimate users create false equivalency, eroding public consensus on vaccination.
Public Health Implications. Directly confronting vaccine skeptics enables bots to legitimize the vaccine debate. More research is needed to determine how best to combat bot-driven content. (Am J Public Health. Published online ahead of print August 23, 2018: e1--e7. doi:10.2105/AJPH.2018.304567)
Gómez-Garc'ia, J-A Moro-Velázquez Laureano; Godino-Llorente, Juan Ignacio; Rusz, Jan; Skodda, Sabine; Arroyave, J R Orozco; Noth, Elmar; Dehak, Najim
Study of the automatic detection of Parkison's Disease based on speaker recognition technologies and allophonic distillation Inproceedings
In: Engineering in Medicine and Biology Society (EMBC),2018 40th Annual International Conference of the IEEE., pp. 1404–1407, 2018.
@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}
}
Moro-Velazquez, Laureano; Gomez-Garcia, Jorge Andres; Godino-Llorente, Juan Ignacio; Villalba, Jesus; Orozco-Arroyave, Juan Rafael; Dehak, Najim
Analysis of speaker recognition methodologies and the influence of kinetic changes to automatically detect Parkinson's Disease Journal Article
In: Applied Soft Computing, vol. 62, pp. 649–666, 2018.
@article{moro2018analysis,
title = {Analysis of speaker recognition methodologies and the influence of kinetic changes to automatically detect Parkinson's Disease},
author = {Laureano Moro-Velazquez and Jorge Andres Gomez-Garcia and Juan Ignacio Godino-Llorente and Jesus Villalba and Juan Rafael Orozco-Arroyave and Najim Dehak},
year = {2018},
date = {2018-01-01},
journal = {Applied Soft Computing},
volume = {62},
pages = {649--666},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Cotterell, Ryan; Kirov, Christo; Hulden, Mans; Eisner, Jason
Quantifying the Trade-off Between Two Types of
Morphological Complexity Inproceedings
In: Proceedings of the Society for Computation in
Linguistics (SCiL), Salt Lake City, 2018.
@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}
}
Wang, Dingquan; Eisner, Jason
Predicting Fine-Grained Syntactic Typology from
Surface Features Inproceedings
In: Proceedings of the Society for Computation in
Linguistics (SCiL), Salt Lake City, 2018.
@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}
}
Eisner, Jason
Probabilistically Modeling Surface Patterns Using
Latent Structure Miscellaneous
Invited talk at the 1st Annual Meeting of the Society
for Computation in Linguistics (SCiL), 2018.
@misc{eisner-2018-scil,
title = {Probabilistically Modeling Surface Patterns Using
Latent Structure},
author = {Jason Eisner},
url = {http://cs.jhu.edu/~jason/papers/#eisner-2018-scil},
year = {2018},
date = {2018-01-01},
address = {Salt Lake City},
howpublished = {Invited talk at the 1st Annual Meeting of the Society
for Computation in Linguistics (SCiL)},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Eisner, Jason; Filardo, Nathaniel Wesley
Treating Machine Learning Algorithms as Declaratively
Specified Circuits Inproceedings
In: Proceedings of the SysML Conference, Palo Alto, 2018.
@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}
}
Mielke, Sebastian J; Eisner, Jason
Spell Once, Summon Anywhere: A Two-Level
Open-Vocabulary Language Model Journal Article
In: Computing Research Repository, 2018.
@article{mielke-eisner-2018-arxiv,
title = {Spell Once, Summon Anywhere: A Two-Level
Open-Vocabulary Language Model},
author = {Sebastian J Mielke and Jason Eisner},
url = {http://cs.jhu.edu/~jason/papers/#mielke-eisner-2018-arxiv},
year = {2018},
date = {2018-01-01},
journal = {Computing Research Repository},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Eisner, Jason
Discrete Latent Variables in NLP: Good, Bad, and
Indifferent Miscellaneous
Invited talk at ACL Workshop on Relevance of
Linguistic Structure in Neural Architectures for NLP, 2018.
@misc{eisner-2018-relsnnlp,
title = {Discrete Latent Variables in NLP: Good, Bad, and
Indifferent},
author = {Jason Eisner},
url = {http://cs.jhu.edu/~jason/papers/#eisner-2018-relsnnlp},
year = {2018},
date = {2018-01-01},
address = {Melbourne},
howpublished = {Invited talk at ACL Workshop on Relevance of
Linguistic Structure in Neural Architectures for NLP},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Wang, Dingquan; Eisner, Jason
Synthetic Data Made to Order: The Case of Parsing Inproceedings
In: Proceedings of the Conference on Empirical Methods in
Natural Language Processing (EMNLP), Brussels, 2018.
@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}
}
Mccree, Alan; Snyder, David; Sell, Gregory; Garcia-Romero, Daniel
Language Recognition for Telephone and Video Speech: The JHU HLTCOE Submission for NIST LRE17 Inproceedings
In: Proc. Odyssey 2018 The Speaker and Language Recognition Workshop, pp. 68–73, 2018.
@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}
}
Sell, Gregory; Duh, Kevin; Snyder, David; Etter, Dave; Garcia-Romero, Daniel
Audio-Visual Person Recognition in Multimedia Data From the Iarpa Janus Program Inproceedings
In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3031–3035, IEEE 2018.
@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}
}
Zhu, Yingke; Ko, Tom; Snyder, David; Mak, Brian; Povey, Daniel
Self-Attentive Speaker Embeddings for Text-Independent Speaker Verification Inproceedings
In: Proc. Interspeech 2018, pp. 3573–3577, 2018.
@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}
}
Silnova, Anna; Brümmer, Niko; Garcia-Romero, Daniel; Snyder, David; Burget, Lukáš
Fast Variational Bayes for Heavy-tailed PLDA Applied to i-vectors and x-vectors Inproceedings
In: Proc. Interspeech 2018, pp. 72–76, 2018.
@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}
}
Sell, Gregory; Snyder, David; McCree, Alan; Garcia-Romero, Daniel; Villalba, Jesús; Maciejewski, Matthew; Manohar, Vimal; Dehak, Najim; Povey, Daniel; Watanabe, Shinji; Khudanpur, Sanjeev
Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge Inproceedings
In: Proc. Interspeech 2018, pp. 2808–2812, 2018.
@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}
}
Snyder, David; Garcia-Romero, Daniel; McCree, Alan; Sell, Gregory; Povey, Daniel; Khudanpur, Sanjeev
Spoken language recognition using x-vectors Inproceedings
In: Odyssey: The Speaker and Language Recognition Workshop, Les Sables d’Olonne, 2018.
@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}
}
Snyder, D; Garcia-Romero, D; Sell, G; Povey, D; Khudanpur, S
X-vectors: Robust DNN Embeddings for Speaker Recognition Inproceedings
In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE 2018.
@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}
}
Subramanian, Aswin Shanmugam; Chen, Szu-Jui; Watanabe, Shinji
Student-Teacher Learning for BLSTM Mask-based Speech Enhancement Inproceedings
In: Proc. Interspeech 2018, pp. 3249–3253, 2018.
@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}
}
Cheng, Gaofeng; Povey, Daniel; Huang, Lu; Xu, Ji; Khudanpur, Sanjeev; Yan, Yonghong
Output-Gate Projected Gated Recurrent Unit for Speech Recognition Journal Article
In: Proc. Interspeech 2018, pp. 1793–1797, 2018.
@article{cheng2018output,
title = {Output-Gate Projected Gated Recurrent Unit for Speech Recognition},
author = {Gaofeng Cheng and Daniel Povey and Lu Huang and Ji Xu and Sanjeev Khudanpur and Yonghong Yan},
year = {2018},
date = {2018-01-01},
journal = {Proc. Interspeech 2018},
pages = {1793--1797},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sarma, Mousmita; Ghahremani, Pegah; Povey, Daniel; Goel, Nagendra Kumar; Sarma, Kandarpa Kumar; Dehak, Najim
Emotion Identification from raw speech signals using DNNs Journal Article
In: Proc. Interspeech 2018, pp. 3097–3101, 2018.
@article{sarma2018emotion,
title = {Emotion Identification from raw speech signals using DNNs},
author = {Mousmita Sarma and Pegah Ghahremani and Daniel Povey and Nagendra Kumar Goel and Kandarpa Kumar Sarma and Najim Dehak},
year = {2018},
date = {2018-01-01},
journal = {Proc. Interspeech 2018},
pages = {3097--3101},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sell, Gregory; Snyder, David; McCree, Alan; Garcia-Romero, Daniel; Villalba, Jes'us; Maciejewski, Matthew; Manohar, Vimal; Dehak, Najim; Povey, Daniel; Watanabe, Shinji; others,
Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge Journal Article
In: Proc. Interspeech 2018, pp. 2808–2812, 2018.
@article{sell2018diarization,
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 Jes{'u}s Villalba and Matthew Maciejewski and Vimal Manohar and Najim Dehak and Daniel Povey and Shinji Watanabe and others},
year = {2018},
date = {2018-01-01},
journal = {Proc. Interspeech 2018},
pages = {2808--2812},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ghahremani, Pegah; Nidadavolu, Phani Sankar; Chen, Nanxin; Villalba, Jes'us; Povey, Daniel; Khudanpur, Sanjeev; Dehak, Najim
End-to-End Deep Neural Network Age Estimation Journal Article
In: Proc. Interspeech 2018, pp. 277–281, 2018.
@article{ghahremani2018end,
title = {End-to-End Deep Neural Network Age Estimation},
author = {Pegah Ghahremani and Phani Sankar Nidadavolu and Nanxin Chen and Jes{'u}s Villalba and Daniel Povey and Sanjeev Khudanpur and Najim Dehak},
year = {2018},
date = {2018-01-01},
journal = {Proc. Interspeech 2018},
pages = {277--281},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ghahremani, Pegah; Hadian, Hossein; Lv, Hang; Povey, Daniel; Khudanpur, Sanjeev
Acoustic Modeling from Frequency Domain Representations of Speech Journal Article
In: Proc. Interspeech 2018, pp. 1596–1600, 2018.
@article{ghahremani2018acoustic,
title = {Acoustic Modeling from Frequency Domain Representations of Speech},
author = {Pegah Ghahremani and Hossein Hadian and Hang Lv and Daniel Povey and Sanjeev Khudanpur},
year = {2018},
date = {2018-01-01},
journal = {Proc. Interspeech 2018},
pages = {1596--1600},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Li, Ke; Xu, Hainan; Wang, Yiming; Povey, Daniel; Khudanpur, Sanjeev
Recurrent Neural Network Language Model Adaptation for Conversational Speech Recognition Journal Article
In: Proc. Interspeech 2018, pp. 3373–3377, 2018.
@article{li2018recurrent,
title = {Recurrent Neural Network Language Model Adaptation for Conversational Speech Recognition},
author = {Ke Li and Hainan Xu and Yiming Wang and Daniel Povey and Sanjeev Khudanpur},
year = {2018},
date = {2018-01-01},
journal = {Proc. Interspeech 2018},
pages = {3373--3377},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Zhu, Yingke; Ko, Tom; Snyder, David; Mak, Brian; Povey, Daniel
Self-Attentive Speaker Embeddings for Text-Independent Speaker Verification Journal Article
In: Proc. Interspeech 2018, pp. 3573–3577, 2018.
@article{zhu2018self,
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},
year = {2018},
date = {2018-01-01},
journal = {Proc. Interspeech 2018},
pages = {3573--3577},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Povey, Daniel; Cheng, Gaofeng; Wang, Yiming; Li, Ke; Xu, Hainan; Yarmohamadi, Mahsa; Khudanpur, Sanjeev
Semi-orthogonal low-rank matrix factorization for deep neural networks Journal Article
In: INTERSPEECH (2018, submitted), 2018.
@article{povey2018semi,
title = {Semi-orthogonal low-rank matrix factorization for deep neural networks},
author = {Daniel Povey and Gaofeng Cheng and Yiming Wang and Ke Li and Hainan Xu and Mahsa Yarmohamadi and Sanjeev Khudanpur},
year = {2018},
date = {2018-01-01},
journal = {INTERSPEECH (2018, submitted)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Chen, Zhehuai; Luitjens, Justin; Xu, Hainan; Wang, Yiming; Povey, Daniel; Khudanpur, Sanjeev
A GPU-based WFST Decoder with Exact Lattice Generation Journal Article
In: arXiv preprint arXiv:1804.03243, 2018.
@article{chen2018gpu,
title = {A GPU-based WFST Decoder with Exact Lattice Generation},
author = {Zhehuai Chen and Justin Luitjens and Hainan Xu and Yiming Wang and Daniel Povey and Sanjeev Khudanpur},
year = {2018},
date = {2018-01-01},
journal = {arXiv preprint arXiv:1804.03243},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Snyder, David; Garcia-Romero, Daniel; McCree, Alan; Sell, Gregory; Povey, Daniel; Khudanpur, Sanjeev
Spoken language recognition using x-vectors Inproceedings
In: Odyssey: The Speaker and Language Recognition Workshop, Les Sables d’Olonne, 2018.
@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}
}
Manohar, Vimal; Hadian, Hossein; Povey, Daniel; Khudanpur, Sanjeev
Semi-supervised training of acoustic models using lattice-free MMI Inproceedings
In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4844–4848, IEEE 2018.
@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}
}
Snyder, David; Garcia-Romero, Daniel; Sell, Gregory; Povey, Daniel; Khudanpur, Sanjeev
X-vectors: Robust DNN embeddings for speaker recognition Journal Article
In: Submitted to ICASSP, 2018.
@article{snyder2018x,
title = {X-vectors: Robust DNN embeddings for speaker recognition},
author = {David Snyder and Daniel Garcia-Romero and Gregory Sell and Daniel Povey and Sanjeev Khudanpur},
year = {2018},
date = {2018-01-01},
journal = {Submitted to ICASSP},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Povey, Daniel; Hadian, Hossein; Ghahremani, Pegah; Li, Ke; Khudanpur, Sanjeev
A Time-Restricted Self-Attention Layer for ASR Journal Article
In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5874-5878, 2018.
@article{Povey2018ATS,
title = {A Time-Restricted Self-Attention Layer for ASR},
author = {Daniel Povey and Hossein Hadian and Pegah Ghahremani and Ke Li and Sanjeev Khudanpur},
year = {2018},
date = {2018-01-01},
journal = {2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {5874-5878},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hadian, Hossein; Sameti, Hossein; Povey, Daniel; Khudanpur, Sanjeev
End-to-end Speech Recognition Using Lattice-free MMI unknown
2018.
@unknown{unknownb,
title = {End-to-end Speech Recognition Using Lattice-free MMI},
author = {Hossein Hadian and Hossein Sameti and Daniel Povey and Sanjeev Khudanpur},
year = {2018},
date = {2018-01-01},
pages = {12-16},
keywords = {},
pubstate = {published},
tppubtype = {unknown}
}
2017
Wang, Dingquan; Peng, Nanyun; Duh, Kevin
A Multi-task Learning Approach to Adapting Bilingual Word Embeddings for Cross-lingual Named Entity Recognition Inproceedings
In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 383–388, Asian Federation of Natural Language Processing, 2017.
@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}
}
Cotterell, Ryan; Duh, Kevin
Low-Resource Named Entity Recognition with Cross-lingual, Character-Level Neural Conditional Random Fields Inproceedings
In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 91–96, Asian Federation of Natural Language Processing, 2017.
@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}
}
Khayrallah, Huda; Kumar, Gaurav; Duh, Kevin; Post, Matt; Koehn, Philipp
Neural Lattice Search for Domain Adaptation in Machine Translation Inproceedings
In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 20–25, Asian Federation of Natural Language Processing, 2017.
@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}
}
White, Aaron Steven; Rastogi, Pushpendre; Duh, Kevin; Durme, Benjamin Van
Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework Inproceedings
In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 996–1005, Asian Federation of Natural Language Processing, 2017.
@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}
}
Shen, Yelong; Liu, Xiaodong; Duh, Kevin; Gao, Jianfeng
An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks Inproceedings
In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 957–966, Asian Federation of Natural Language Processing, 2017.
@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}
}
Zhang, Sheng; Duh, Kevin; Durme, Benjamin Van
Selective Decoding for Cross-lingual Open Information Extraction Inproceedings
In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 832–842, Asian Federation of Natural Language Processing, 2017.
@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}
}
Ding, Shuoyang; Khayrallah, Huda; Koehn, Philipp; Post, Matt; Kumar, Gaurav; Duh, Kevin
The JHU Machine Translation Systems for WMT 2017 Inproceedings
In: Proceedings of the Second Conference on Machine Translation, pp. 276–282, Association for Computational Linguistics, Copenhagen, Denmark, 2017.
@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}
}
Nadejde, Maria; Reddy, Siva; Sennrich, Rico; Dwojak, Tomasz; Junczys-Dowmunt, Marcin; Koehn, Philipp; Birch, Alexandra
Predicting Target Language CCG Supertags Improves Neural Machine Translation Inproceedings
In: Proceedings of the Second Conference on Machine Translation, pp. 68–79, Association for Computational Linguistics, Copenhagen, Denmark, 2017.
@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}
}
Bojar, Ondřej; Chatterjee, Rajen; Federmann, Christian; Graham, Yvette; Haddow, Barry; Huang, Shujian; Huck, Matthias; Koehn, Philipp; Liu, Qun; Logacheva, Varvara; Monz, Christof; Negri, Matteo; Post, Matt; Rubino, Raphael; Specia, Lucia; Turchi, Marco
Findings of the 2017 Conference on Machine Translation (WMT17) Inproceedings
In: Proceedings of the Second Conference on Machine Translation, pp. 169–214, Association for Computational Linguistics, Copenhagen, Denmark, 2017.
@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}
}
Xu, Hainan; Koehn, Philipp
Zipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora Inproceedings
In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2935–2940, Association for Computational Linguistics, Copenhagen, Denmark, 2017.
@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}
}
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.
Renduchintala, Adithya; Koehn, Philipp; Eisner, Jason
Knowledge Tracing in Sequential Learning of Inflected Vocabulary Inproceedings
In: Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pp. 238–247, Association for Computational Linguistics, Vancouver, Canada, 2017.
@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}
}
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.
Koehn, Philipp; Knowles, Rebecca
Six Challenges for Neural Machine Translation Inproceedings
In: Proceedings of the First Workshop on Neural Machine Translation, pp. 28–39, Association for Computational Linguistics, Vancouver, 2017.
@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}
}
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.
Sakaguchi, Keisuke; Post, Matt; Durme, Benjamin Van
Error-repair Dependency Parsing for Ungrammatical Texts Inproceedings
In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 189–195, Association for Computational Linguistics, Vancouver, Canada, 2017.
@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}
}
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.
1345 entries « ‹ 2 of 27
› »
