Tatsuya Aoyama


2022

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Probe-Less Probing of BERT’s Layer-Wise Linguistic Knowledge with Masked Word Prediction
Tatsuya Aoyama | Nathan Schneider
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop

The current study quantitatively (and qualitatively for an illustrative purpose) analyzes BERT’s layer-wise masked word prediction on an English corpus, and finds that (1) the layerwise localization of linguistic knowledge primarily shown in probing studies is replicated in a behavior-based design and (2) that syntactic and semantic information is encoded at different layers for words of different syntactic categories. Hypothesizing that the above results are correlated with the number of likely potential candidates of the masked word prediction, we also investigate how the results differ for tokens within multiword expressions.

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Comparing Native and Learner Englishes Using a Large Pre-trained Language Model
Tatsuya Aoyama
Proceedings of the 11th Workshop on NLP for Computer Assisted Language Learning