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Aligning parallel sentences in multilingual corpora is essential to curatingdata for downstream applications such as Machine Translation. In this work, wepresent OneAligner, an alignment model specially designed for sentenceretrieval tasks. This model is able to train on only one language pair andtransfers, in a cross-lingual fashion, to low-resource language pairs withnegligible degradation in performance. When trained with all language pairs ofa large-scale parallel multilingual corpus (OPUS-100), this model achieves thestate-of-the-art result on the Tateoba dataset, outperforming an equally-sizedprevious model by 8.0 points in accuracy while using less than 0.6% of theirparallel data. When finetuned on a single rich-resource language pair, be itEnglish-centered or not, our model is able to match the performance of the onesfinetuned on all language pairs under the same data budget with less than 2.0points decrease in accuracy. Furthermore, with the same setup, scaling up thenumber of rich-resource language pairs monotonically improves the performance,reaching a minimum of 0.4 points discrepancy in accuracy, making it lessmandatory to collect any low-resource parallel data. Finally, we concludethrough empirical results and analyses that the performance of the sentencealignment task depends mostly on the monolingual and parallel data size, up toa certain size threshold, rather than on what language pairs are used fortraining or evaluation.

OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval