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Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories
Wenlin Yao, Xiaoman Pan, Lifeng Jin, Jianshu Chen, Dian Yu, Dong Yu
Word Sense Disambiguation (WSD) aims to automatically identify the exact meaning of one word according to its context. Existing supervised models struggle to make correct predictions on rare word senses due to limited training data and can only select the best definition sentence from one predefined word sense inventory (e.g., WordNet). To address the data sparsity problem and generalize the model to be independent of one predefined inventory, we propose a gloss alignment algorithm that can align definition sentences (glosses) with the same meaning from different sense inventories to collect rich lexical knowledge. We then train a model to identify semantic equivalence between a target word in context and one of its glosses using these aligned inventories, which exhibits strong transfer capability to many WSD tasks. Experiments on benchmark datasets show that the proposed method improves predictions on both frequent and rare word senses, outperforming prior work by 1.2% on the All-Words WSD Task and 4.3% on the Low-Shot WSD Task. Evaluation on WiC Task also indicates that our method can better capture word meanings in context.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Word Sense Disambiguation | FEWS | SEMeq base | F1 (Fewshot Test) | 80.1 | #3 of 8 | Archive leaderboard | report |
| Word Sense Disambiguation | FEWS | SEMeq base | F1 (Zero shot test) | 70.2 | #3 of 8 | Archive leaderboard | report |
| Word Sense Disambiguation | FEWS | SEMeq base | F1 (Zeroshot Dev) | 71.5 | #3 of 8 | Archive leaderboard | report |
| Word Sense Disambiguation | FEWS | SEMeq base | F1(FewShot Dev) | 80.4 | #3 of 8 | Archive leaderboard | report |
| Word Sense Disambiguation | FEWS | SEMEq Large | F1 (Fewshot Test) | 82.3 | #4 of 8 | Archive leaderboard | report |
| Word Sense Disambiguation | FEWS | SEMEq Large | F1 (Zero shot test) | 72.2 | #4 of 8 | Archive leaderboard | report |
| Word Sense Disambiguation | FEWS | SEMEq Large | F1 (Zeroshot Dev) | 73.7 | #4 of 8 | Archive leaderboard | report |
| Word Sense Disambiguation | FEWS | SEMEq Large | F1(FewShot Dev) | 81.8 | #4 of 8 | 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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