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- W4287661888 abstract "Domain adaptation or transfer learning using pre-trained language models such as BERT has proven to be an effective approach for many natural language processing tasks. In this work, we propose to formulate word sense disambiguation as a relevance ranking task, and fine-tune BERT on sequence-pair ranking task to select the most probable sense definition given a context sentence and a list of candidate sense definitions. We also introduce a data augmentation technique for WSD using existing example sentences from WordNet. Using the proposed training objective and data augmentation technique, our models are able to achieve state-of-the-art results on the English all-words benchmark datasets." @default.
- W4287661888 created "2022-07-25" @default.
- W4287661888 creator A5054460729 @default.
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- W4287661888 date "2020-09-24" @default.
- W4287661888 modified "2023-09-24" @default.
- W4287661888 title "Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences" @default.
- W4287661888 doi "https://doi.org/10.48550/arxiv.2009.11795" @default.
- W4287661888 hasPublicationYear "2020" @default.
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