Cited by 30Natural Language Processing TechniquesTopic ModelingSpeech and dialogue systems

Automatic generation of short answer questions for reading comprehension assessment

HUANG, YAN, HE, LIANZHEN·2016·Natural Language Engineering

Abstract

Writing items for reading comprehension assessment is time-consuming. Automating part of the process can help test-designers to develop assessments more efficiently and consistently. This paper presents an approach to automatically generating short answer questions for reading comprehension assessment. Our major contribution is to introduce Lexical Functional Grammar (LFG) as the linguistic framework for question generation, which enables systematic utilization of semantic and syntactic information. The approach can efficiently generate questions of better quality than previous high-performing question generation systems, and uses paraphrasing and sentence selection to improve the cognitive complexity and effectiveness of questions.

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Registry

Crossref · 10.1017/s1351324915000455

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71 of 71 references verified0 citation errors · 0 not checkable

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Authors
HUANG, Y., & HE, L.
Affiliations
  • University of Cambridge (GB)
  • Zhejiang University (CN)
Venue
Natural Language Engineering
Published
2016-01-13
Volume
22
Issue
3
Pages
457-489
ISSN
  • 1351-3249 (ISSN-L)
  • 1469-8110
OpenAlex
W2336465850
Cited by
30
References
75
Citations by year
2026: 2 · 2025: 5 · 2024: 6 · 2023: 7 · 2022: 1 · 2021: 3 · 2019: 3 · 2018: 3
FWCI
1.65
Type
journal article
Language
en
Open access
closed
Locations
Natural Language Engineering (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
Keywords
automatic question generation, short-answer questions, reading comprehension assessment, Lexical-Functional Grammar, test item generation, paraphrase, sentence selection, cognitive complexity

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