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In this work, we retrained the distilled BERT language model for Walmart's voice shopping assistant on retail domain-specific data.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Lee, J.; Yoon, W.; Kim, S.; Kim, D.; Kim, S.; So, C. H.; and Kang, J. 2019 · 1901
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SciBERT: Pretrained Contextualized Embeddings for Scientific Text
Beltagy, I.; Cohan, A.; and Lo, K. 2019 · 1903
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Publicly Available Clinical BERT Embeddings
Alsentzer, E.; Murphy, J. R.; Boag, W.; Weng, W.; Jin, D.; Naumann, T.; and McDermott, M. B. A. 2019 · 1904
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
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Natural Language Understanding with the Quora Question Pairs Dataset
Sharma, L.; Graesser, L.; Nangia, N.; and Evci, U. 2019 · 1907
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Well-read students learn better: On the importance of pre-training compact models
Turc, I.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 1908
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Tinybert: Distilling bert for natural language understanding
Jiao, X.; Yin, Y.; Shang, L.; Jiang, X.; Chen, X.; Li, L.; Wang, F.; and Liu, Q. 2019 · 1909
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Albert: A lite bert for self-supervised learning of language representations
Lan, Z.; Chen, M.; Goodman, S.; Gimpel, K.; Sharma, P.; and Soricut, R. 2019 · 1909
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V.; Debut, L.; Chaumond, J.; and Wolf, T. 2019 · 1910
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SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation
Cer, D.; Diab, M.; Agirre, E.; Lopez-Gazpio, I.; and Specia, L. 2017 · 2001
Cited alongside, same era.
FastBERT: a Self-distilling BERT with Adaptive Inference Time
Liu, W.; Zhou, P.; Zhao, Z.; Wang, Z.; Deng, H.; and Ju, Q. 2020 · 2004
Cited alongside, same era.
Language models are few-shot learners
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 2005
Cited alongside, same era.
A Dynamic Oracle for Arc-Eager Dependency Parsing
Goldberg, Y.; and Nivre, J. 2012 · 2012
Cited alongside, same era.
Dependency-based word embeddings
Levy, O.; and Goldberg, Y. 2014 · 2014
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Williams, A.; Nangia, N.; and Bowman, S. R. 2017 · 2017
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.; Lee, K.; and Toutanova, K. 2018 · 2018
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Deep Contextualized Word Representations
Peters, M.; Neumann, M.; Iyyer, M.; Gardner, M.; Clark, C.; Lee, K.; and Zettlemoyer, L. 2018 · 2018
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Improving language understanding by generative pre-training
Radford, A.; Narasimhan, K.; Salimans, T.; and Sutskever, I. 2018 · 2018
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Know what you don’t know: Unanswerable questions for SQuAD
Rajpurkar, P.; Jia, R.; and Liang, P. 2018 · 2018
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Sentiment analysis on conversational texts
Ojamaa, B.; Jokinen, K.; and Muischenk, K. 2015 · 2015
Cited alongside, same era.
Dependency Based Embeddings for Sentence Classification Tasks
Komninos, A.; and Manandhar, S. 2016 · 2016
Cited alongside, same era.
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Neural network acceptability judgments
Warstadt, A.; Singh, A.; and Bowman, S. R. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Yang, Z.; Dai, Z.; Yang, Y.; Carbonell, J.; Salakhutdinov, R. R.; and Le, Q. V. 2019 · 2019
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Recent advances and challenges in task-oriented dialog systems
Zhang, Z.; Takanobu, R.; Zhu, Q.; Huang, M.; and Zhu, X. 2020 · 2020
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