Ranking paragraphs foriImproving answer recall in open-domain Question Answering

Jinhyuk Lee, Seongjun Yun, Hyunjae Kim, Miyoung Ko, Jaewoo Kang

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

9 Citations (Scopus)

Abstract

Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source. As open-domain QA requires retrieving relevant documents from text corpora to answer questions, its performance largely depends on the performance of document retrievers. However, since traditional information retrieval systems are not effective in obtaining documents with a high probability of containing answers, they lower the performance of QA systems. Simply extracting more documents increases the number of irrelevant documents, which also degrades the performance of QA systems. In this paper, we introduce Paragraph Ranker which ranks paragraphs of retrieved documents for a higher answer recall with less noise. We show that ranking paragraphs and aggregating answers using Paragraph Ranker improves performance of open-domain QA pipeline on the four open-domain QA datasets by 7.8% on average.

Original languageEnglish
Title of host publicationProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
EditorsEllen Riloff, David Chiang, Julia Hockenmaier, Jun'ichi Tsujii
PublisherAssociation for Computational Linguistics
Pages565-569
Number of pages5
ISBN (Electronic)9781948087841
Publication statusPublished - 2020 Jan 1
Event2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 - Brussels, Belgium
Duration: 2018 Oct 312018 Nov 4

Publication series

NameProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018

Conference

Conference2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
CountryBelgium
CityBrussels
Period18/10/3118/11/4

ASJC Scopus subject areas

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

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  • Cite this

    Lee, J., Yun, S., Kim, H., Ko, M., & Kang, J. (2020). Ranking paragraphs foriImproving answer recall in open-domain Question Answering. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 (pp. 565-569). (Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018). Association for Computational Linguistics.