Abstract
In this paper, we propose LaPraDoR, a pretrained dual-tower dense retriever that does not require any supervised data for training. Specifically, we first present Iterative Contrastive Learning (ICoL) that iteratively trains the query and document encoders with a cache mechanism. ICoL not only enlarges the number of negative instances but also keeps representations of cached examples in the same hidden space. We then propose Lexicon-Enhanced Dense Retrieval (LEDR) as a simple yet effective way to enhance dense retrieval with lexical matching. We evaluate LaPraDoR on the recently proposed BEIR benchmark, including 18 datasets of 9 zero-shot text retrieval tasks. Experimental results show that LaPraDoR achieves state-of-the-art performance compared with supervised dense retrieval models, and further analysis reveals the effectiveness of our training strategy and objectives. Compared to re-ranking, our lexicon-enhanced approach can be run in milliseconds (22.5x faster) while achieving superior performance.- Anthology ID:
- 2022.findings-acl.281
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2022
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3557–3569
- Language:
- URL:
- https://aclanthology.org/2022.findings-acl.281
- DOI:
- 10.18653/v1/2022.findings-acl.281
- Cite (ACL):
- Canwen Xu, Daya Guo, Nan Duan, and Julian McAuley. 2022. LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3557–3569, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval (Xu et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.findings-acl.281.pdf
- Code
- jetrunner/laprador
- Data
- BEIR, C4, CLIMATE-FEVER, FEVER, HotpotQA, MS MARCO, Natural Questions, SciFact