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Word Sense Disambiguation (WSD) aims to automatically identify the exactmeaning of one word according to its context. Existing supervised modelsstruggle to make correct predictions on rare word senses due to limitedtraining data and can only select the best definition sentence from onepredefined word sense inventory (e.g., WordNet). To address the data sparsityproblem 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 collectrich lexical knowledge. We then train a model to identify semantic equivalencebetween a target word in context and one of its glosses using these alignedinventories, which exhibits strong transfer capability to many WSD tasks.Experiments on benchmark datasets show that the proposed method improvespredictions on both frequent and rare word senses, outperforming prior work by1.2% on the All-Words WSD Task and 4.3% on the Low-Shot WSD Task. Evaluation onWiC Task also indicates that our method can better capture word meanings incontext.

Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories