{"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/cluse-cross-lingual-unsupervised-sense","title":"CLUSE: Cross-Lingual Unsupervised Sense Embeddings","arxiv_id":"1809.05694","date":"2018-09-15","proceeding":"EMNLP 2018 10","authors":["Ta-Chung Chi","Yun-Nung Chen"],"abstract":"This paper proposes a modularized sense induction and representation learning\nmodel that jointly learns bilingual sense embeddings that align well in the\nvector space, where the cross-lingual signal in the English-Chinese parallel\ncorpus is exploited to capture the collocation and distributed characteristics\nin the language pair. The model is evaluated on the Stanford Contextual Word\nSimilarity (SCWS) dataset to ensure the quality of monolingual sense\nembeddings. In addition, we introduce Bilingual Contextual Word Similarity\n(BCWS), a large and high-quality dataset for evaluating cross-lingual sense\nembeddings, which is the first attempt of measuring whether the learned\nembeddings are indeed aligned well in the vector space. The proposed approach\nshows the superior quality of sense embeddings evaluated in both monolingual\nand bilingual spaces.","url_abs":"http://arxiv.org/abs/1809.05694v2","url_pdf":"http://arxiv.org/pdf/1809.05694v2.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":"cluse-cross-lingual-unsupervised-sense","repo_url":"https://github.com/MiuLab/CLUSE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"word-similarity","task_name":"Word Similarity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.05694","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}