Document alignment is necessary for the hierarchical mining (Ba\~n\'on 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 Guzm\'an, 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).

翻译：文档对齐是分层挖掘（Bañon等人，2020；Morishita等人，2022）的必要步骤，其目标是在同一网络域内跨源语言和目标语言对齐文档。目前已开发出多种基于句子嵌入的高精度方法，例如TK-PERT（Thompson和Koehn，2020）与最优传输（OT）（Clark等人，2019；El-Kishky和Guzmán，2020）。然而，考虑到网络挖掘数据的海量规模，必须同时兼顾准确性与处理速度。本文提出一种用于计算文档间相似度的跨语言双向最大相似度评分（BiMax），以提升OT方法的计算效率。实验表明，在WMT16双语文档对齐任务中，BiMax在达到与OT相当准确率的同时，实现了约100倍的速度提升。此外，本文还对当前最先进的多语言句子嵌入模型进行了全面的性能分析。文中所有对齐方法均已开源为名为EmbDA的工具（https://github.com/EternalEdenn/EmbDA）。