Question generation through transfer learning

Yin Hsiang Liao, Jia Ling Koh

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

Abstract

An automatic question generation (QG) system aims to produce questions from a text, such as a sentence or a paragraph. Traditional approaches are mainly based on heuristic, hand-crafted rules to transduce a declarative sentence to a related interrogative sentence. However, creating such a set of rules requires deep linguistic knowledge and most of these rules are language-specific. Although a data-driven approach reduces the participation of linguistic experts, to get sufficient labeled data for QG model training is still a difficult task. In this paper, we applied a neural sequence-to-sequence pointer-generator network with various transfer learning strategies to capture the underlying information of making a question, on a target domain with rare training pairs. Our experiment demonstrates the viability of domain adaptation in QG task. We also show the possibility that transfer learning is helpful in a semi-supervised approach when the amount of training pairs in the target QG dataset is not large enough.

Original languageEnglish
Title of host publicationTrends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings
EditorsHamido Fujita, Jun Sasaki, Philippe Fournier-Viger, Moonis Ali
PublisherSpringer Science and Business Media Deutschland GmbH
Pages3-17
Number of pages15
ISBN (Print)9783030557881
DOIs
Publication statusPublished - 2020
Event33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020 - Kitakyushu, Japan
Duration: 2020 Sep 222020 Sep 25

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12144 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020
Country/TerritoryJapan
CityKitakyushu
Period2020/09/222020/09/25

Keywords

  • Question generation
  • Sequence-to-sequence model
  • Transfer learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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