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English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings

2022-11-11 11:17:56
Yau-Shian Wang, Ashley Wu, Graham Neubig
arXiv_CL
           
Abstract

Universal cross-lingual sentence embeddings map semantically similar cross-lingual sentences into a shared embedding space. Aligning cross-lingual sentence embeddings usually requires supervised cross-lingual parallel sentences. In this work, we propose mSimCSE, which extends SimCSE to multilingual settings and reveal that contrastive learning on English data can surprisingly learn high-quality universal cross-lingual sentence embeddings without any parallel data. In unsupervised and weakly supervised settings, mSimCSE significantly improves previous sentence embedding methods on cross-lingual retrieval and multilingual STS tasks. The performance of unsupervised mSimCSE is comparable to fully supervised methods in retrieving low-resource languages and multilingual STS. The performance can be further enhanced when cross-lingual NLI data is available. Our code is publicly available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2211.06127

PDF

https://arxiv.org/pdf/2211.06127.pdf

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