Bitext Mining for Low-Resource Languages via Contrastive Learning

Philipp Koehn; Weiting Tan

Bitext Mining for Low-Resource Languages via Contrastive Learning

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arxiv 2208.11194 v1 pith:2ZHTQXHA submitted 2022-08-23 cs.CL

Weiting Tan , Philipp Koehn This is my paper · ORCID

classification cs.CL

keywords languagesbitextscontrastivelow-resourceminingapproachbitextchallenging

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Mining high-quality bitexts for low-resource languages is challenging. This paper shows that sentence representation of language models fine-tuned with multiple negatives ranking loss, a contrastive objective, helps retrieve clean bitexts. Experiments show that parallel data mined from our approach substantially outperform the previous state-of-the-art method on low resource languages Khmer and Pashto.

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