The importance of qualitative parallel data in machine translation has long
been determined but it has always been very difficult to obtain such in
sufficient quantity for the majority of world languages, mainly because of the
associated cost and also the lack of accessibility to these languages. Despite
the potential for obtaining parallel datasets from online articles using
automatic approaches, forensic investigations have found a lot of
quality-related issues such as misalignment, and wrong language codes. In this
work, we present a simple but qualitative parallel sentence aligner that
carefully leveraged the closed-access Cohere multilingual embedding, a solution
that ranked second in the just concluded #CoHereAIHack 2023 Challenge (see
this https URL). The proposed approach achieved $94.96$ and
$54.83$ f1 scores on FLORES and MAFAND-MT, compared to $3.64$ and $0.64$ of
LASER respectively. Our method also achieved an improvement of more than 5 BLEU
scores over LASER, when the resulting datasets were used with MAFAND-MT dataset
to train translation models. Our code and data are available for research
purposes here (this https URL).

我们提出了一个简单而定性的平行句子对齐算法，利用闭源 Cohere 多语言嵌入，通过与 MAFAND-MT 数据集训练翻译模型，在 FLORES 和 MAFAND-MT 上分别实现了 $94.96$ 和 $54.83$ 的 f1 分数，相较于 LASER，BLEU 分数提升了超过 5 个单位。