Improving Passage Retrieval with Zero-Shot Question Generation

• URL: http://arxiv.org/abs/2204.07496v4
• Date: Mon, 3 Apr 2023 00:07:58 GMT
• Title: Improving Passage Retrieval with Zero-Shot Question Generation
• Authors: Devendra Singh Sachan and Mike Lewis and Mandar Joshi and Armen Aghajanyan and Wen-tau Yih and Joelle Pineau and Luke Zettlemoyer
• Abstract summary: We propose a simple and effective re-ranking method for improving passage retrieval in open question answering. The re-ranker re-scores retrieved passages with a zero-shot question generation model, which uses a pre-trained language model to compute the probability of the input question conditioned on a retrieved passage.
• Score: 109.11542468380331
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a simple and effective re-ranking method for improving passage retrieval in open question answering. The re-ranker re-scores retrieved passages with a zero-shot question generation model, which uses a pre-trained language model to compute the probability of the input question conditioned on a retrieved passage. This approach can be applied on top of any retrieval method (e.g. neural or keyword-based), does not require any domain- or task-specific training (and therefore is expected to generalize better to data distribution shifts), and provides rich cross-attention between query and passage (i.e. it must explain every token in the question). When evaluated on a number of open-domain retrieval datasets, our re-ranker improves strong unsupervised retrieval models by 6%-18% absolute and strong supervised models by up to 12% in terms of top-20 passage retrieval accuracy. We also obtain new state-of-the-art results on full open-domain question answering by simply adding the new re-ranker to existing models with no further changes.

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