DualReward: A Dynamic Reinforcement Learning Framework for Cloze Tests Distractor Generation

• URL: http://arxiv.org/abs/2507.11875v1
• Date: Wed, 16 Jul 2025 03:39:36 GMT
• Title: DualReward: A Dynamic Reinforcement Learning Framework for Cloze Tests Distractor Generation
• Authors: Tianyou Huang, Xinglu Chen, Jingshen Zhang, Xinying Qiu, Ruiying Niu,
• Abstract summary: DualReward is a novel reinforcement learning framework for automatic distractor generation in cloze tests.<n>We evaluate our approach on both passage-level (CLOTH-F) and sentence-level (MCQ) cloze test datasets.
• Score: 0.4660328753262075
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper introduces DualReward, a novel reinforcement learning framework for automatic distractor generation in cloze tests. Unlike conventional approaches that rely primarily on supervised learning or static generative models, our method employs a dual reward structure with adaptive scaling that differentiates between human-created gold standard distractors and model-generated candidates. The framework dynamically adjusts reward signal intensity based on model performance and confidence. We evaluate our approach on both passage-level (CLOTH-F) and sentence-level (MCQ) cloze test datasets, demonstrating consistent improvements over state-of-the-art baselines. Experimental results show that our adaptive reward scaling mechanism provides modest but consistent benefits on homogeneous datasets (CLOTH-F) and more substantial improvements (3.48-3.86% in P@1) on diverse, cross-domain data (MCQ), suggesting its particular effectiveness for handling varied question types and domains. Our work offers a flexible framework that effectively balances learning from reliable human examples while exploring novel, high-quality distractors for automated test generation.

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