B i M ax: Bidirectional M ax S im Score for Document-Level Alignment (2025)

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.

## introduction document alignment is the task of finding parallel document pairs, which are identified as translations of each other, within a collection of documents. it is mainly employed as a preparatory stage within hierarchical mining for parallel sentence pair curation @xcite @xcite , seeking to enhance pair quality @xcite by restricting sentence alignment in high-precision aligned document pairs. with recent advances in document-level machine translation @xcite @xcite , document alignment has also become a viable strategy for developing high-quality parallel document pairs @xcite . there are four mainstream approaches: url matching @xcite , bilingual lexicon (azpeitia and @xcite , machine translation @xcite , sentence embedding @xcite @xcite . @xcite proposed the overlapping fixed-length segmentation (ofls) as an alternative to sentence-based segmentation (sbs) for generating embeddings. when applied to mean-pool, tk-pert @xcite , and ot @xcite , this strategy led to both speed and accuracy improvements. among these methods, ot achieves the highest recall in the wmt16 bilingual document alignment shared task (buck and koehn, 2016a) based on @xcite . however, the computation of ot inherently involves an optimization process, necessitating multiple iterative operations. this results in high computational complexity, limiting its performance in speed. thus, we propose the bidirectional maxsim score (bimax), which matches the maximum similarity between a given segment and the opposed segment collection and then sums and averages the similarity scores. the implementation is computationally efficient, requiring only a single similarity matrix computation followed by two max-pooling operations. this idea is inspired by the maxsim score in @xcite , which uses a late interaction mechanism to reduce the query-passage computational cost by calculating only the maximum similarity for each query token relative to the tokens in the passage. we extend this score to the sentence level and make it bidirectional. additionally, we evaluate combinations of stateof-the-art embedding models (i.e., models that perform well in tasks such as bitext mining and sts) with various segmentation strategies and document alignment methods on the small-scale ja-en mnrn dataset @xcite , aiming to find suitable models and methods for different scenarios. meanwhile, we make a modest attempt to examine the performance of different methods on low-resource languages. finally, we build a downstream mt benchmark foot_1 to assess the impact of document alignment based on the wmt23 parallel data curation task, and the construction process is comprehensively and transparently recorded in appendix g. ## related work currently, there are four mainstream approaches to document alignment. the first involves simply calculating similarity based on the urls of the documents @xcite . the second uses a bag-of-words or bag-of-ngrams representation of the document contents, leveraging a bilingual lexicon for computation @xcite . the third entails translating documents into the same language, followed by similarity calculations using ngram-based metrics (e.g., bleu) @xcite . the fourth utilizes multilingual pre-trained embedding models to map documents into a shared vector space, where similarity is determined by the distances between vectors @xcite @xcite . in the wmt16 bilingual document alignment shared task @xcite , numerous techniques and systems were proposed. however, due to the limitations of technology at the time, all efforts focused on the first three approaches, with no exploration of embedding-based methods. with the development of pre-trained multilingual sentence embedding models @xcite , which map sentences from different languages into a shared multilingual vector space, cross-lingual bitext mining has become feasible. this progress also facilitates representing documents using segment embeddings and computing doc-pair similarities via vector-based methods. @xcite introduced tk-pert, a method that assigns weights to sentences using regionally emphasized windows derived from a modified pert distribution @xcite to form document feature vectors. optimal transport (ot) was also applied in cross-lingual document alignment, evolving from the word level 3 method unlike maxsim utilized in @xcite , which uses the query's hidden word embeddings to search for the most similar token in the passage undirectionally, we apply it to sentence-level as the bidirectional maxsim score (bimax), introducing the following key modifications: (1) transforming from monolingual to cross-lingual, (2) shifting from word-level embeddings to sentence-level embeddings, and (3) moving from one-sided maximum similarity matching to a bidirectional approach. ## analysis of document alignment performance on mnrn we use the ja-en mnrn dataset to conduct the analysis under various sentence embedding models, three segmentation strategies, sbs 4 , blob 5 @xcite , and ofls 6 @xcite , and four document alignment methods, focusing on three main points: (1) which model is suitable for which segmentation strategy, (2) how do different document alignment methods perform under each model, and (3) which combination of these three factors yields the best results. the reasons for selecting embedding models and the model settings are recorded in appendix a and b, while the experimental setup and the details of the evaluation metrics are described in appendix c. 3 because the english documents contain duplicates, the number of gold pairs exceeds that of the japanese documents. we present the statistical information for four models under various segmentation strategies in ## conclusion this paper introduces a novel and efficient bimax score for the document alignment task, reducing computational complexity compared to ot. however, while bimax shows the best performance on the fernando dataset and the small-scale mnrn dataset, results from the wmt16 document alignment task reveal that we cannot definitively assert bimax's accuracy surpasses ot or tk-pert. instead, we advocate for bimax primarily for its efficiency in scenarios such as processing large-scale web-crawled data. in these cases, according to our analysis of experiments, the labse + ofls + bi-max approach is recommended, as it outperforms all other combinations.