Answering Any-hop Open-domain Questions with Iterative Document Reranking

16 Sep 2020  ·  Ping Nie, Yuyu Zhang, Arun Ramamurthy, Le Song ·

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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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Question Answering HotpotQA DDRQA ANS-EM 0.625 # 15
ANS-F1 0.759 # 16
SUP-EM 0.510 # 18
SUP-F1 0.789 # 19
JOINT-EM 0.360 # 23
JOINT-F1 0.639 # 17


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