Micron: Multigranular interaction for contextualizing representation in non-factoid question answering

Hojae Han, Seungtaek Choi, Haeju Park, Seung Won Hwang

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

Abstract

This paper studies the problem of non-factoid question answering, where the answer may span over multiple sentences. Existing solutions can be categorized into representation- and interaction-focused approaches. We combine their complementary strength, by a hybrid approach allowing multi-granular interactions, but represented at word level, enabling an easy integration with strong word-level signals. Specifically, we propose MICRON: Multigranular Interaction for Contextualizing RepresentatiON, a novel approach which derives contextualized uni-gram representation from n-grams. Our contributions are as follows: First, we enable multi-granular matches between question and answer n-grams. Second, by contextualizing word representation with surrounding n-grams, MICRON can naturally utilize word-based signals for query term weighting, known to be effective in information retrieval. We validate MICRON in two public non-factoid question answering datasets: WikiPassageQA and InsuranceQA, showing our model achieves the state of the art among baselines with reported performances on both datasets.

Original languageEnglish
Title of host publicationEMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference
PublisherAssociation for Computational Linguistics
Pages5890-5895
Number of pages6
ISBN (Electronic)9781950737901
Publication statusPublished - 2020 Jan 1
Event2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019 - Hong Kong, China
Duration: 2019 Nov 32019 Nov 7

Publication series

NameEMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference

Conference

Conference2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019
CountryChina
CityHong Kong
Period19/11/319/11/7

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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  • Cite this

    Han, H., Choi, S., Park, H., & Hwang, S. W. (2020). Micron: Multigranular interaction for contextualizing representation in non-factoid question answering. In EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference (pp. 5890-5895). (EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference). Association for Computational Linguistics.