Open-domain multi-hop question answering (QA) requires to retrieve multiple supporting documents, some of which have little lexical overlap with the question and can only be located by iterative document retrieval. However , multi-step document retrieval often incurs more relevant but non-supporting documents, which dampens the downstream noise-sensitive reader module for answer extraction. To address this challenge , we propose Dynamic Document Reranking (DDR) to iteratively retrieve, rerank and filter documents, and adaptively determine when to stop the retrieval process. DDR employs an entity-linked document graph for multi-document interaction, which boosts up the retrieval performance. Experiments on HotpotQA full wiki setting show that our method achieves more than 7 points higher reranking performance over the previous best retrieval model, and also achieves state-of-the-art question answering performance on the official leaderboard .
Existing approaches for open-domain question answering (QA) are typically designed for questions that require either single-hop or multi-hop reasoning, which make strong assumptions of the complexity of questions to be answered. Also , multi-step document retrieval often incurs higher number of relevant but non-supporting documents, which dampens the downstream noise-sensitive reader module for answer extraction. To address these challenges , we propose a unified QA framework to answer any-hop open-domain questions, which iteratively retrieves, reranks and filters documents, and adaptively determines when to stop the retrieval process. To improve the retrieval accuracy, we propose a graph-based reranking model that perform multi-document interaction as the core of our iterative reranking framework. Our method consistently achieves performance comparable to or better than the state-of-the-art on both single-hop and multi-hop open-domain QA datasets, including Natural Questions Open, SQuAD Open, and HotpotQA .
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Open-domain multi-hop
Existing approaches for open-domain
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requires to retrieve multiple supporting documents, some of which have little lexical overlap with the question and can only be located by iterative document retrieval. However
are typically designed for questions that require either single-hop or multi-hop reasoning, which make strong assumptions of the complexity of questions to be answered. Also
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Dynamic Document Reranking (DDR) to iteratively retrieve, rerank and filter
a unified QA framework to answer any-hop open-domain questions, which iteratively retrieves, reranks and filters
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determine
determines
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DDR employs an entity-linked document graph for
To improve the retrieval accuracy, we propose a graph-based reranking model that perform
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interaction, which boosts up the retrieval performance. Experiments on HotpotQA full wiki setting show that our method achieves more than 7 points higher reranking performance over the previous best retrieval model, and also achieves
interaction as the core of our iterative reranking framework. Our method consistently achieves performance comparable to or better than the
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question answering performance on the official leaderboard
on both single-hop and multi-hop open-domain QA datasets, including Natural Questions Open, SQuAD Open, and HotpotQA