Abstract
Dense retrieval has shown great success for passage ranking in English. However, its effectiveness for non-English languages remains unexplored due to limitation in training resources. In this work, we explore different transfer techniques for document ranking from English annotations to non-English languages. Our experiments reveal that zero-shot model-based transfer using mBERT improves search quality. We find that weakly-supervised target language transfer is competitive compared to generation-based target language transfer, which requires translation models.- Anthology ID:
- 2021.mrl-1.24
- Volume:
- Proceedings of the 1st Workshop on Multilingual Representation Learning
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Venue:
- MRL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 251–253
- Language:
- URL:
- https://aclanthology.org/2021.mrl-1.24
- DOI:
- 10.18653/v1/2021.mrl-1.24
- Cite (ACL):
- Peng Shi, Rui Zhang, He Bai, and Jimmy Lin. 2021. Cross-Lingual Training of Dense Retrievers for Document Retrieval. In Proceedings of the 1st Workshop on Multilingual Representation Learning, pages 251–253, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Cross-Lingual Training of Dense Retrievers for Document Retrieval (Shi et al., MRL 2021)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2021.mrl-1.24.pdf