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Contextual language models (CLMs) have pushed the NLP benchmarks to a new height.
Tenney, I.; Xia, P.; Chen, B.; Wang, A.; Poliak, A.; McCoy, R. T.; Kim, N.; Van Durme, B.; Bowman, S. R.; Das, D.; et al. 2019 · 1905
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HuggingFace’s Transformers: State-of-the-art Natural Language Processing
Wolf, T.; Debut, L.; Sanh, V.; Chaumond, J.; Delangue, C.; Moi, A.; Cistac, P.; Rault, T.; Louf, R.; Funtowicz, M.; and Brew, J. 2019 · 1910
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Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
Zhao, J.; Wang, T.; Yatskar, M.; Ordonez, V.; and Chang, K.-W. 2018a · 2003
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Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems
Kiritchenko, S.; and Mohammad, S. 2018 · 2005
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Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research
Poria, S.; Hazarika, D.; Majumder, N.; and Mihalcea, R. 2020 · 2005
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Convolutional Neural Networks for Sentence Classification
Kim, Y. 2014 · 2014
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Bolukbasi, T.; Chang, K.-W.; Zou, J. Y.; Saligrama, V.; and Kalai, A. T. 2016 · 2016
Cited alongside, same era.
Semantics derived automatically from language corpora contain human-like biases
Caliskan, A.; Bryson, J. J.; and Narayanan, A. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
Cited alongside, same era.
SemEval-2018 Task 1: Affect in Tweets
Mohammad, S.; Bravo-Marquez, F.; Salameh, M.; and Kiritchenko, S. 2018 · 2018
Cited alongside, same era.
Learning Gender-Neutral Word Embeddings
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings
Basta, C.; Costa-jussà, M. R.; and Casas, N. 2019 · 2019
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Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them
Gonen, H.; and Goldberg, Y. 2019 · 2019
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Measuring Bias in Contextualized Word Representations
Kurita, K.; Vyas, N.; Pareek, A.; Black, A. W.; and Tsvetkov, Y. 2019 · 2019
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Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
Lu, J.; Batra, D.; Parikh, D.; and Lee, S. 2019 · 2019
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Gender Bias in Pretrained Swedish Embeddings
Sahlgren, M.; and Olsson, F. 2019 · 2019
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Gender Bias in Contextualized Word Embeddings
Zhao, J.; Wang, T.; Yatskar, M.; Cotterell, R.; Ordonez, V.; and Chang, K.-W. 2019 · 2019
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Zhao, J.; Zhou, Y.; Li, Z.; Wang, W.; and Chang, K.-W. 2018b · 2018
Cited alongside, same era.
Learning gender-neutral word embeddings
Zhao, J.; Zhou, Y.; Li, Z.; Wang, W.; and Chang, K.-W. 2018c
Cited in the paper.
Later among the works it cites.