2017/03/26 by Yusuke Watanabe, Bhuwan Dhingra, Watanabe, Yusuke +3 · 17 citations
Computer Science · #Advanced Graph Neural Networks #Comprehension #Computer science #Information retrieval #Natural Language Processing Techniques #Natural language processing #Programming language #Question answering #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.1703.08885
published in arXiv (Cornell University) (Cornell University)
arxiv created 2017/03/26 · openalex publication_date 2017/03/26 · arxiv updated 2017/03/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Open domain Question Answering (QA) systems must interact with external knowledge sources, such as web pages, to find relevant information. Information sources like Wikipedia, however, are not well structured and difficult to utilize in comparison with Knowledge Bases (KBs). In this work we present a two-step approach to question answering from unstructured text, consisting of a retrieval step and a comprehension step. For comprehension, we present an RNN based attention model with a novel mixture mechanism for selecting answers from either retrieved articles or a fixed vocabulary. For retrieval we introduce a hand-crafted model and a neural model for ranking relevant articles. We achieve state-of-the-art performance on W IKI M OVIES dataset, reducing the error by 40%. Our experimental results further demonstrate the importance of each of the introduced components.