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arXiv · 1908.05646

SenseBERT: Driving Some Sense into BERT

Yoav Levine·Barak Lenz·Or Dagan·Ori Ram·Dan Padnos·Or Sharir·Shai Shalev-Shwartz·Amnon Shashua·Yoav Shoham
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Abstract

The ability to learn from large unlabeled corpora has allowed neural language models to advance the frontier in natural language understanding. However, existing self-supervision techniques operate at the word form level, which serves as a surrogate for the underlying semantic content. This paper proposes a method to employ weak-supervision directly at the word sense level. Our model, named SenseBERT, is pre-trained to predict not only the masked words but also their WordNet supersenses. Accordingly, we attain a lexical-semantic level language model, without the use of human annotation. SenseBERT achieves significantly improved lexical understanding, as we demonstrate by experimenting on SemEval Word Sense Disambiguation, and by attaining a state of the art result on the Word in Context task.

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Yoav Levine, Barak Lenz, Or Dagan, Ori Ram, Dan Padnos, Or Sharir, Shai Shalev-Shwartz, Amnon Shashua, Yoav Shoham. 2019-08-15. SenseBERT: Driving Some Sense into BERT. https://arxiv.org/abs/1908.05646

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