High-quality distractor generation framework for English reading comprehension

Zhengrong Guo*, Hui Wang, Gongde Guo*

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Distractors are widely used in tasks such as reading comprehension and subject education. Generating high-quality distractors automatically poses a challenging task. Existing frameworks for generating distractors in the context of English reading comprehension primarily rely on training deep neural networks with given passages, questions, and corresponding answers. However, in more difficult questions, these frameworks often produce distractors with low levels of distraction, limiting their practical applications. To address this issue, we propose a framework called 'Generating Distractors Framework with High Distraction' (GDF-HD) that automatically generates high-distraction distractors. The GDF-HD framework categorizes common English reading comprehension questions into three types: detail questions, summary questions, and inference questions. Different question types utilize distinct approaches for generating distractors, and the framework enhances the distraction of these distractors from both syntactic and semantic perspectives. To validate the feasibility and effectiveness of our framework, we conduct experiments and evaluations using the RACE dataset. Additionally, we employ manual assessments to evaluate the quality and distraction levels of the generated distractors. The experimental results demonstrate that the GDF-HD framework is capable of producing high-quality distractors and significantly increasing their distraction levels.

Original languageEnglish
Title of host publication2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023: Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages331-338
Number of pages8
ISBN (Electronic)9798350357738
ISBN (Print)9798350357745
DOIs
Publication statusPublished - 25 Apr 2024
Event5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023 - Hybrid, Guangzhou, China
Duration: 08 Dec 202310 Dec 2023

Publication series

NameInternational Academic Exchange Conference on Science and Technology Innovation, IAECST: Proceedings

Conference

Conference5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023
Country/TerritoryChina
CityHybrid, Guangzhou
Period08/12/202310/12/2023

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • distraction
  • distractors generation
  • knowledge reasoning
  • reading comprehension

ASJC Scopus subject areas

  • Numerical Analysis
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Signal Processing
  • Software
  • Computational Mechanics
  • Electrical and Electronic Engineering

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