{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adapting-bert-for-word-sense-disambiguation","title":"Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences","arxiv_id":"2009.11795","date":"2020-09-24","proceeding":"Findings of the Association for Computational Linguistics 2020","authors":["Boon Peng Yap","Andrew Koh","Eng Siong Chng"],"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. 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