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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages4656-4667
Number of pages12
ISBN (Electronic)9781952148255
DOIs
StatePublished - 2020
Externally publishedYes
Event58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 - Virtual, Online, United States
Duration: 5 Jul 202010 Jul 2020

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics

Conference

Conference58th Annual Meeting of the Association for Computational Linguistics, ACL 2020
Country/TerritoryUnited States
CityVirtual, Online
Period5/07/2010/07/20

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Computer Science Applications
  • Linguistics and Language

Fingerprint

Dive into the research topics of 'SenseBERT: Driving some sense into BERT'. Together they form a unique fingerprint.

Cite this