Papers › SenseBERT: Driving Some Sense into BERT
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
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Natural Language Inference | QNLI | SenseBERT-base 110M | Accuracy | 90.6% | #33 of 43 | Archive leaderboard | report |
| Natural Language Inference | RTE | SenseBERT-base 110M | Accuracy | 67.5% | #62 of 90 | Archive leaderboard | report |
| Word Sense Disambiguation | Words in Context | SenseBERT-large 340M | Accuracy | 72.1 | #11 of 37 | Archive leaderboard | report |
| Word Sense Disambiguation | Words in Context | SenseBERT-base 110M | Accuracy | 70.3 | #12 of 37 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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