LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval

• URL: http://arxiv.org/abs/2203.06169v1
• Date: Fri, 11 Mar 2022 18:53:12 GMT
• Title: LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval
• Authors: Canwen Xu and Daya Guo and Nan Duan and Julian McAuley
• Abstract summary: Experimental results show that LaPraDoR achieves state-of-the-art performance compared with supervised dense retrieval models. Compared to re-ranking, our lexicon-enhanced approach can be run in milliseconds (22.5x faster) while achieving superior performance.
• Score: 55.097573036580066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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