Multi-step Reasoning for Open-domain Question Answering


Sep 27, 2018 (modified: Oct 10, 2018) ICLR 2019 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: This paper introduces a new framework for open-domain question answering in which the retriever and the reader \emph{iteratively} interact with each other. The framework is agnostic to the architecture of the machine reading model and the retriever uses fast nearest neighbor search algorithms that allow it to scale to corpus containing millions of paragraphs. We show the efficacy of our architecture by achieving state-of-the-art results on large open domain datasets such as TriviaQA-unfiltered \citep{joshi2017triviaqa}. We also show that our multi-step-reasoning framework brings uniform improvements when applied to two different reader architectures.
  • Keywords: Open domain Question Answering, Reinforcement Learning, Query reformulation
  • TL;DR: Paragraph retriever and machine reader interacts with each other via reinforcement learning to yield large improvements on open domain datasets
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