DualReward: A Dynamic Reinforcement Learning Framework for Cloze Tests Distractor Generation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916845387251712 |
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| author | Huang, Tianyou Chen, Xinglu Zhang, Jingshen Qiu, Xinying Niu, Ruiying |
| author_facet | Huang, Tianyou Chen, Xinglu Zhang, Jingshen Qiu, Xinying Niu, Ruiying |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11875 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DualReward: A Dynamic Reinforcement Learning Framework for Cloze Tests Distractor Generation Huang, Tianyou Chen, Xinglu Zhang, Jingshen Qiu, Xinying Niu, Ruiying Computation and Language 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. |
| title | DualReward: A Dynamic Reinforcement Learning Framework for Cloze Tests Distractor Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.11875 |