Multilingual Representation Distillation with Contrastive Learning

• URL: http://arxiv.org/abs/2210.05033v2
• Date: Sun, 30 Apr 2023 20:21:57 GMT
• Title: Multilingual Representation Distillation with Contrastive Learning
• Authors: Weiting Tan, Kevin Heffernan, Holger Schwenk and Philipp Koehn
• Abstract summary: We integrate contrastive learning into multilingual representation distillation and use it for quality estimation of parallel sentences. We validate our approach with multilingual similarity search and corpus filtering tasks.
• Score: 20.715534360712425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multilingual sentence representations from large models encode semantic information from two or more languages and can be used for different cross-lingual information retrieval and matching tasks. In this paper, we integrate contrastive learning into multilingual representation distillation and use it for quality estimation of parallel sentences (i.e., find semantically similar sentences that can be used as translations of each other). We validate our approach with multilingual similarity search and corpus filtering tasks. Experiments across different low-resource languages show that our method greatly outperforms previous sentence encoders such as LASER, LASER3, and LaBSE.

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