{"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/learning-multilingual-word-embeddings-in","title":"Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach","arxiv_id":"1808.08773","date":"2018-08-27","proceeding":"TACL 2019 3","authors":["Pratik Jawanpuria","Arjun Balgovind","Anoop Kunchukuttan","Bamdev Mishra"],"abstract":"We propose a novel geometric approach for learning bilingual mappings given\nmonolingual embeddings and a bilingual dictionary. Our approach decouples\nlearning the transformation from the source language to the target language\ninto (a) learning rotations for language-specific embeddings to align them to a\ncommon space, and (b) learning a similarity metric in the common space to model\nsimilarities between the embeddings. We model the bilingual mapping problem as\nan optimization problem on smooth Riemannian manifolds. We show that our\napproach outperforms previous approaches on the bilingual lexicon induction and\ncross-lingual word similarity tasks. We also generalize our framework to\nrepresent multiple languages in a common latent space. In particular, the\nlatent space representations for several languages are learned jointly, given\nbilingual dictionaries for multiple language pairs. We illustrate the\neffectiveness of joint learning for multiple languages in zero-shot word\ntranslation setting. Our implementation is available at\nhttps://github.com/anoopkunchukuttan/geomm .","url_abs":"http://arxiv.org/abs/1808.08773v3","url_pdf":"http://arxiv.org/pdf/1808.08773v3.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":"learning-multilingual-word-embeddings-in","repo_url":"https://github.com/anoopkunchukuttan/geomm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"learning-multilingual-word-embeddings-in","repo_url":"https://github.com/microsoft/recommenders","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"bilingual-lexicon-induction","task_name":"Bilingual Lexicon Induction"},{"task_slug":"multilingual-word-embeddings","task_name":"Multilingual Word Embeddings"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"},{"task_slug":"word-similarity","task_name":"Word Similarity"},{"task_slug":"word-translation","task_name":"Word Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.08773","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}