{"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/rapo-an-adaptive-ranking-paradigm-for","title":"RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction","arxiv_id":"2210.09926","date":"2022-10-18","proceeding":null,"authors":["Zhoujin Tian","Chaozhuo Li","Shuo Ren","Zhiqiang Zuo","Zengxuan Wen","Xinyue Hu","Xiao Han","Haizhen Huang","Denvy Deng","Qi Zhang","Xing Xie"],"abstract":"Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages. Existing approaches generally focus on minimizing the distances between words in the aligned pairs, while suffering from low discriminative capability to distinguish the relative orders between positive and negative candidates. In addition, the mapping function is globally shared by all words, whose performance might be hindered by the deviations in the distributions of different languages. In this work, we propose a novel ranking-oriented induction model RAPO to learn personalized mapping function for each word. RAPO is capable of enjoying the merits from the unique characteristics of a single word and the cross-language isomorphism simultaneously. Extensive experimental results on public datasets including both rich-resource and low-resource languages demonstrate the superiority of our proposal. Our code is publicly available in \\url{https://github.com/Jlfj345wf/RAPO}.","url_abs":"https://arxiv.org/abs/2210.09926v1","url_pdf":"https://arxiv.org/pdf/2210.09926v1.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":"rapo-an-adaptive-ranking-paradigm-for","repo_url":"https://github.com/jlfj345wf/rapo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"bilingual-lexicon-induction","task_name":"Bilingual Lexicon Induction"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2210.09926","atlas_url":"https://app.syntology.ai/?focus=2210.09926","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09926"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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