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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.