@inproceedings{huang-etal-2025-dualreward,
    title = "{D}ual{R}eward: A Dynamic Reinforcement Learning Framework for Cloze Tests Distractor Generation",
    author = "Huang, Tianyou  and
      Chen, Xinglu  and
      Zhang, Jingshen  and
      Qiu, Xin Ying  and
      Niu, Ruiying",
    editor = "Sun, Maosong  and
      Duan, Peiyong  and
      Liu, Zhiyuan  and
      Xu, Ruifeng  and
      Sun, Weiwei",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-1.84/",
    pages = "1122--1135",
    abstract = "``This paper introduces DualReward, a novel reinforcement learning framework for automatic dis-tractor generation in cloze tests. Unlike conventional approaches that rely primarily on super-vised 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 overstate-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), suggest-ing 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.''"
}