  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.
