Knowledge Enhanced Pre-training for Cross-lingual Dense Retrieval
Hang Zhang, Yeyun Gong, Dayiheng Liu, Shunyu Zhang, Xingwei He, Jiancheng Lv, Jian Guo
Abstract
In recent years, multilingual pre-trained language models (mPLMs) have achieved significant progress in cross-lingual dense retrieval. However, most mPLMs neglect the importance of knowledge. Knowledge always conveys similar semantic concepts in a language-agnostic manner, while query-passage pairs in cross-lingual retrieval also share common factual information. Motivated by this observation, we introduce KEPT, a novel mPLM that effectively leverages knowledge to learn language-agnostic semantic representations. To achieve this, we construct a multilingual knowledge base using hyperlinks and cross-language page alignment data annotated by Wiki. From this knowledge base, we mine intra- and cross-language pairs by extracting symmetrically linked segments and multilingual entity descriptions. Subsequently, we adopt contrastive learning with the mined pairs to pre-train KEPT. We evaluate KEPT on three widely-used benchmarks, considering both zero-shot cross-lingual transfer and supervised multilingual fine-tuning scenarios. Extensive experimental results demonstrate that KEPT achieves strong multilingual and cross-lingual retrieval performance with significant improvements over existing mPLMs.- Anthology ID:
- 2024.lrec-main.857
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 9810–9821
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.857
- DOI:
- Cite (ACL):
- Hang Zhang, Yeyun Gong, Dayiheng Liu, Shunyu Zhang, Xingwei He, Jiancheng Lv, and Jian Guo. 2024. Knowledge Enhanced Pre-training for Cross-lingual Dense Retrieval. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 9810–9821, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Knowledge Enhanced Pre-training for Cross-lingual Dense Retrieval (Zhang et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2024.lrec-main.857.pdf