A context-aware attention network for interactive question answering

Huayu Li, Martin Renqiang Min, Yong Ge, Asim Kadav

Research output: Chapter in Book/Report/Conference proceedingConference contribution

44 Scopus citations

Abstract

Neural network based sequence-to-sequence models in an encoderdecoder framework have been successfully applied to solve Question Answering (QA) problems, predicting answers from statements and questions. However, almost all previous models have failed to consider detailed context information and unknown states under which systems do not have enough information to answer given questions. These scenarios with incomplete or ambiguous information are very common in the setting of Interactive Question Answering (IQA). To address this challenge, we develop a novel model, employing context-dependent word-level attention for more accurate statement representations and question-guided sentencelevel attention for better context modeling. We also generate unique IQA datasets to test our model, which will be made publicly available. Employing these attention mechanisms, our model accurately understands when it can output an answer or when it requires generating a supplementary question for additional input depending on different contexts. When available, user's feedback is encoded and directly applied to update sentence-level attention to infer an answer. Extensive experiments on QA and IQA datasets quantitatively demonstrate the effectiveness of our model with significant improvement over state-of-the-art conventional QA models.

Original languageEnglish (US)
Title of host publicationKDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages927-935
Number of pages9
ISBN (Electronic)9781450348874
DOIs
StatePublished - Aug 13 2017
Event23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 - Halifax, Canada
Duration: Aug 13 2017Aug 17 2017

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
VolumePart F129685

Conference

Conference23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017
Country/TerritoryCanada
CityHalifax
Period8/13/178/17/17

Keywords

  • Attention
  • Interactive question answering
  • Question answering
  • Recurrent neural network

ASJC Scopus subject areas

  • Software
  • Information Systems

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