{"ID":2854076,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.15577","arxiv_id":"2510.15577","title":"BiMax: Bidirectional MaxSim Score for Document-Level Alignment","abstract":"Document alignment is necessary for the hierarchical mining (Bañón et al., 2020; Morishita et al., 2022), which aligns documents across source and target languages within the same web domain. Several high precision sentence embedding-based methods have been developed, such as TK-PERT (Thompson and Koehn, 2020) and Optimal Transport (OT) (Clark et al., 2019; El-Kishky and Guzmán, 2020). However, given the massive scale of web mining data, both accuracy and speed must be considered. In this paper, we propose a cross-lingual Bidirectional Maxsim score (BiMax) for computing doc-to-doc similarity, to improve efficiency compared to the OT method. Consequently, on the WMT16 bilingual document alignment task, BiMax attains accuracy comparable to OT with an approximate 100-fold speed increase. Meanwhile, we also conduct a comprehensive analysis to investigate the performance of current state-of-the-art multilingual sentence embedding models. All the alignment methods in this paper are publicly available as a tool called EmbDA (https://github.com/EternalEdenn/EmbDA).","short_abstract":"Document alignment is necessary for the hierarchical mining (Bañón et al., 2020; Morishita et al., 2022), which aligns documents across source and target languages within the same web domain. Several high precision sentence embedding-based methods have been developed, such as TK-PERT (Thompson and Koehn, 2020) and Opti...","url_abs":"https://arxiv.org/abs/2510.15577","url_pdf":"https://arxiv.org/pdf/2510.15577v1","authors":"[\"Xiaotian Wang\",\"Takehito Utsuro\",\"Masaaki Nagata\"]","published":"2025-10-17T12:16:25Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[]","has_code":false,"code_links":[{"ID":608115,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2854076,"paper_url":"https://arxiv.org/abs/2510.15577","paper_title":"BiMax: Bidirectional MaxSim Score for Document-Level Alignment","repo_url":"https://github.com/EternalEdenn/EmbDA","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}