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In modern interactive speech-based systems, speech is consumed and transcribed incrementally prior to having disfluencies removed.
Switchboard: telephone speech corpus for research and development
J.J. Godfrey, E.C. Holliman, and J. McDaniel. 1992 · 1992
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A prosody only decision-tree model for disfluency detection
Elizabeth Shriberg, Rebecca Bates, and Andreas Stolcke. 1997 · 1997
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Treebank-3
Mitchell P. Marcus, Beatrice Santorini, Mary Ann Marcinkiewicz, and Ann Taylor. 1999 · 1999
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Edit detection and parsing for transcribed speech
Eugene Charniak and Mark Johnson. 2001 · 2001
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Detecting speech repairs incrementally using a noisy channel approach
Simon Zwarts, Mark Johnson, and Robert Dale. 2010 · 2010
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Evaluation and optimisation of incremental processors
Timo Baumann Okko Buß and David Schlangen. 2011 · 2011
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Segmentation and disfluency removal for conversational speech translation
Hany Hassan, Lee Schwartz, Dilek Hakkani-Tür, and Gokhan Tur. 2014 · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015 · 2015
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Recurrent Neural Networks for Incremental Disfluency Detection
Julian Hough and David Schlangen. 2015 · 2015
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
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Disfluency detection using a bidirectional LSTM
Vicky Zayats, Mari Ostendorf, and Hannaneh Hajishirzi. 2016 · 2016
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Google vizier: A service for black-box optimization
Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, and D. Sculley. 2017 · 2017
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Joint, incremental disfluency detection and utterance segmentation from speech
Julian Hough and David Schlangen. 2017 · 2017
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Challenging neural dialogue models with natural data: Memory networks fail on incremental phenomena
Igor Shalyminov, Arash Eshghi, and Oliver Lemon. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Know what you don’t know: Unanswerable questions for SQuAD
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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Multi-task learning for domain-general spoken disfluency detection in dialogue systems
Incremental processing in the age of non-incremental encoders: An empirical assessment of bidirectional models for incremental NLU
Brielen Madureira and David Schlangen. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Re-framing incremental deep language models for dialogue processing with multi-task learning
Morteza Rohanian and Julian Hough. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Disfl-QA: A benchmark dataset for understanding disfluencies in question answering
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Igor Shalyminov, Arash Eshghi, and Oliver Lemon. 2018 · 2018
Cited alongside, same era.
Noisy BiLSTM-based models for disfluency detection
Nguyen Bach and Fei Huang. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
Qian Chen, Mengzhe Chen, Bo Li, and Wen Wang. 2020 · 2020
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Aditya Gupta, Jiacheng Xu, Shyam Upadhyay, Diyi Yang, and Manaal Faruqui. 2021 · 2021
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Towards incremental transformers: An empirical analysis of transformer models for incremental nlu
Patrick Kahardipraja, Brielen Madureira, and David Schlangen. 2021 · 2021
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Disfluency Detection with Unlabeled Data and Small BERT Models
Johann C. Rocholl, Vicky Zayats, Daniel D. Walker, Noah B. Murad, Aaron Schneider, and Daniel J. Liebling. 2021 · 2021
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Best of both worlds: Making high accuracy non-incremental transformer-based disfluency detection incremental
Morteza Rohanian and Julian Hough. 2021 · 2021
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Extremely small BERT models from mixed-vocabulary training
Sanqiang Zhao, Raghav Gupta, Yang Song, and Denny Zhou. 2021 · 2021
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End-to-end speech recognition and disfluency removal
Paria Jamshid Lou and Mark Johnson. 2020 · 2061
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