Abstract
We tackle the challenge of cross-lingual training of neural document ranking models for mono-lingual retrieval, specifically leveraging relevance judgments in English to improve search in non-English languages. Our work successfully applies multi-lingual BERT (mBERT) to document ranking and additionally compares against a number of alternatives: translating the training data, translating documents, multi-stage hybrids, and ensembles. Experiments on test collections in six different languages from diverse language families reveal many interesting findings: model-based relevance transfer using mBERT can significantly improve search quality in (non-English) mono-lingual retrieval, but other “low resource” approaches are competitive as well.- Anthology ID:
- 2020.findings-emnlp.249
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2768–2773
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.249
- DOI:
- 10.18653/v1/2020.findings-emnlp.249
- Cite (ACL):
- Peng Shi, He Bai, and Jimmy Lin. 2020. Cross-Lingual Training of Neural Models for Document Ranking. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2768–2773, Online. Association for Computational Linguistics.
- Cite (Informal):
- Cross-Lingual Training of Neural Models for Document Ranking (Shi et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/2020.findings-emnlp.249.pdf