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        <title>BiMax: Bidirectional MaxSim Score for Document-Level Alignment</title>
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    <abstract>Document alignment is necessary for the hierarchical mining, 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 and Optimal Transport (OT). 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.</abstract>
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%0 Conference Proceedings
%T BiMax: Bidirectional MaxSim Score for Document-Level Alignment
%A Wang, Xiaotian
%A Utsuro, Takehito
%A Nagata, Masaaki
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F wang-etal-2025-bimax
%X Document alignment is necessary for the hierarchical mining, 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 and Optimal Transport (OT). 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.
%U https://aclanthology.org/2025.findings-emnlp.704/
%P 13095-13116

Markdown (Informal)

[BiMax: Bidirectional MaxSim Score for Document-Level Alignment](https://aclanthology.org/2025.findings-emnlp.704/) (Wang et al., Findings 2025)

• BiMax: Bidirectional MaxSim Score for Document-Level Alignment (Wang et al., Findings 2025)

ACL

• Xiaotian Wang, Takehito Utsuro, and Masaaki Nagata. 2025. BiMax: Bidirectional MaxSim Score for Document-Level Alignment. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 13095–13116, Suzhou, China. Association for Computational Linguistics.