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

By Yuyu Zhang, Ping Nie, Arun Ramamurthy, Le Song. 2020

24 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Retrieval & Search, Question Answering & Reading Comprehension

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In Artificial Intelligence

  1. Chenliang Li
  2. Songfang Huang
  3. Bin Bi
  4. Ming Yan
  5. Wei Wang
  6. Fei Huang
  7. Luo Si
  8. Haiyang Xu

In Computer Vision and Pattern Recognition

  1. Chenliang Li
  2. Songfang Huang
  3. Bin Bi
  4. Ming Yan
  5. Fei Huang
  6. Wei Wang
  7. Haiyang Xu
  8. Luo Si

In Information Retrieval & Search

  1. Ming Yan
  2. Songfang Huang
  3. Wei Wang

Citations in the corpus