  Aligning parallel sentences in multilingual corpora is essential to curating
data for downstream applications such as Machine Translation. In this work, we
present OneAligner, an alignment model specially designed for sentence
retrieval tasks. This model is able to train on only one language pair and
transfers, in a cross-lingual fashion, to low-resource language pairs with
negligible degradation in performance. When trained with all language pairs of
a large-scale parallel multilingual corpus (OPUS-100), this model achieves the
state-of-the-art result on the Tateoba dataset, outperforming an equally-sized
previous model by 8.0 points in accuracy while using less than 0.6% of their
parallel data. When finetuned on a single rich-resource language pair, be it
English-centered or not, our model is able to match the performance of the ones
finetuned on all language pairs under the same data budget with less than 2.0
points decrease in accuracy. Furthermore, with the same setup, scaling up the
number of rich-resource language pairs monotonically improves the performance,
reaching a minimum of 0.4 points discrepancy in accuracy, making it less
mandatory to collect any low-resource parallel data. Finally, we conclude
through empirical results and analyses that the performance of the sentence
alignment task depends mostly on the monolingual and parallel data size, up to
a certain size threshold, rather than on what language pairs are used for
training or evaluation.
