Multilingual Representation Distillation with Contrastive Learning
This work addresses the need for better multilingual sentence representations for cross-lingual information retrieval and matching, particularly in low-resource settings, and is incremental as it builds on existing distillation methods.
The paper tackles the problem of improving multilingual sentence representations for cross-lingual tasks by integrating contrastive learning into distillation, resulting in significant performance gains over previous encoders like LASER, LASER3, and LaBSE in low-resource languages.
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.