L2R-QA: An Open-Domain Question Answering Framework

  • Tieke He
  • Yu Li
  • Zhipeng Zou
  • Qing WuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11936)


Open-domain question answering has always being a challenging task. It involves information retrieval, natural language processing, machine learning, and so on. In this work, we try to explore some comparable methods in improving the precision of open-domain question answering. In detail, we bring in the topic model in the phase of document retrieval, in the hope of exploiting more hidden semantic information of a document. Also, we incorporate the learning to rank model into the LSTM to train more available features for the ranking of candidate paragraphs. Specifically, we combine the results from both LSTM and learning to rank model, which lead to a more precise understanding of questions, as well as the paragraphs. We conduct an extensive set of experiments to evaluate the efficacy of our proposed framework, which proves to be superior.


Question answering Learning to rank Topic model 



The work is supported in part by the National Key Research and Development Program of China (2016YFC0800805) and the National Natural Science Foundation of China (61772014).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China
  2. 2.School of EconomicsNanjingPeople’s Republic of China

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