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I assess the extent to which the recently introduced BERT model captures English syntactic phenomena, using (1) naturally-occurring subject-verb agreement stimuli; (2) "coloreless green ideas" subject-verb agreement stimuli, in which content words in natural sentences are randomly replaced with words sharing the same part-of-speech and inflection; and (3) manually crafted stimuli for subject-verb agreement and reflexive anaphora phenomena.
Assessing the ability of LSTMs to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016 · 2016
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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
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Colorless green recurrent networks dream hierarchically
Kristina Gulordava, Piotr Bojanowski, Edouard Grave, Tal Linzen, and Marco Baroni. 2018 · 2018
Cited alongside, same era.
Targeted syntactic evaluation of language models
Rebecca Marvin and Tal Linzen. 2018 · 2018
Cited alongside, same era.
Why self-attention? a targeted evaluation of neural machine translation architectures
Gongbo Tang, Mathias Müller, Annette Rios, and Rico Sennrich. 2018 · 2018
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The importance of being recurrent for modeling hierarchical structure
Ke Tran, Arianna Bisazza, and Christof Monz. 2018 · 2018
Later among the works it cites.
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