{"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/cross-lingual-document-retrieval-using","title":"Cross-lingual Document Retrieval using Regularized Wasserstein Distance","arxiv_id":"1805.04437","date":"2018-05-11","proceeding":null,"authors":["Georgios Balikas","Charlotte Laclau","Ievgen Redko","Massih-Reza Amini"],"abstract":"Many information retrieval algorithms rely on the notion of a good distance\nthat allows to efficiently compare objects of different nature. Recently, a new\npromising metric called Word Mover's Distance was proposed to measure the\ndivergence between text passages. In this paper, we demonstrate that this\nmetric can be extended to incorporate term-weighting schemes and provide more\naccurate and computationally efficient matching between documents using\nentropic regularization. We evaluate the benefits of both extensions in the\ntask of cross-lingual document retrieval (CLDR). Our experimental results on\neight CLDR problems suggest that the proposed methods achieve remarkable\nimprovements in terms of Mean Reciprocal Rank compared to several baselines.","url_abs":"http://arxiv.org/abs/1805.04437v1","url_pdf":"http://arxiv.org/pdf/1805.04437v1.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":"cross-lingual-document-retrieval-using","repo_url":"https://github.com/balikasg/WassersteinRetrieval","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"information-retrieval","task_name":"Information Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}