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.- Anthology ID:
- 2022.emnlp-main.621
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9122–9133
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.621
- DOI:
- 10.18653/v1/2022.emnlp-main.621
- Cite (ACL):
- Yaushian Wang, Ashley Wu, and Graham Neubig. 2022. English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9122–9133, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings (Wang et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.621.pdf