MICRON: Multigranular Interaction for Contextualizing RepresentatiON in Non-factoid Question Answering

Hojae Han, Seungtaek Choi, Haeju Park, Seung-won Hwang


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.
Anthology ID:
D19-1601
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
5890–5895
Language:
URL:
https://aclanthology.org/D19-1601
DOI:
10.18653/v1/D19-1601
Bibkey:
Cite (ACL):
Hojae Han, Seungtaek Choi, Haeju Park, and Seung-won Hwang. 2019. MICRON: Multigranular Interaction for Contextualizing RepresentatiON in Non-factoid Question Answering. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5890–5895, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
MICRON: Multigranular Interaction for Contextualizing RepresentatiON in Non-factoid Question Answering (Han et al., EMNLP 2019)
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PDF:
https://aclanthology.org/D19-1601.pdf