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 language | English |
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Title of host publication | 2023 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023: Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 331-338 |
Number of pages | 8 |
ISBN (Electronic) | 9798350357738 |
ISBN (Print) | 9798350357745 |
DOIs | |
Publication status | Published - 25 Apr 2024 |
Event | 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023 - Hybrid, Guangzhou, China Duration: 08 Dec 2023 → 10 Dec 2023 |
Publication series
Name | International Academic Exchange Conference on Science and Technology Innovation, IAECST: Proceedings |
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Conference
Conference | 5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023 |
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Country/Territory | China |
City | Hybrid, Guangzhou |
Period | 08/12/2023 → 10/12/2023 |
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