Query-as-context Pre-training for Dense Passage Retrieval

• URL: http://arxiv.org/abs/2212.09598v3
• Date: Sun, 15 Oct 2023 03:43:53 GMT
• Title: Query-as-context Pre-training for Dense Passage Retrieval
• Authors: Xing Wu, Guangyuan Ma, Wanhui Qian, Zijia Lin, Songlin Hu
• Abstract summary: Methods have been developed to improve the performance of dense passage retrieval by using context-supervised pre-training. This paper proposes query-as-context pre-training, a simple yet effective pre-training technique to alleviate the issue.
• Score: 27.733665432319803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, methods have been developed to improve the performance of dense passage retrieval by using context-supervised pre-training. These methods simply consider two passages from the same document to be relevant, without taking into account the possibility of weakly correlated pairs. Thus, this paper proposes query-as-context pre-training, a simple yet effective pre-training technique to alleviate the issue. Query-as-context pre-training assumes that the query derived from a passage is more likely to be relevant to that passage and forms a passage-query pair. These passage-query pairs are then used in contrastive or generative context-supervised pre-training. The pre-trained models are evaluated on large-scale passage retrieval benchmarks and out-of-domain zero-shot benchmarks. Experimental results show that query-as-context pre-training brings considerable gains and meanwhile speeds up training, demonstrating its effectiveness and efficiency. Our code will be available at https://github.com/caskcsg/ir/tree/main/cotmae-qc .

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