{"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/conditional-generators-of-words-definitions","title":"Conditional Generators of Words Definitions","arxiv_id":"1806.10090","date":"2018-06-26","proceeding":"ACL 2018 7","authors":["Artyom Gadetsky","Ilya Yakubovskiy","Dmitry Vetrov"],"abstract":"We explore recently introduced definition modeling technique that provided\nthe tool for evaluation of different distributed vector representations of\nwords through modeling dictionary definitions of words. In this work, we study\nthe problem of word ambiguities in definition modeling and propose a possible\nsolution by employing latent variable modeling and soft attention mechanisms.\nOur quantitative and qualitative evaluation and analysis of the model shows\nthat taking into account words ambiguity and polysemy leads to performance\nimprovement.","url_abs":"http://arxiv.org/abs/1806.10090v1","url_pdf":"http://arxiv.org/pdf/1806.10090v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"conditional-generators-of-words-definitions","repo_url":"https://github.com/agadetsky/pytorch-definitions","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.10090","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}