Bitext Mining for Low-Resource Languages via Contrastive Learning

作     者：Tan, Weiting Koehn, Philipp 

作者机构：Center for Language and Speech Processing Computer Science Department Johns Hopkins University United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

摘      要：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. © 2022, CC BY.