提出首个支持解释生成的越南语多选阅读理解方法
ViMultiChoice: Toward a Method That Gives Explanation for Multiple-Choice Reading Comprehension in Vietnamese
- 联合预测答案与生成解释，提升模型可解释性
- 在ViMMRC 2.0和新数据集上达到当前最佳性能
- 解释生成能显著提升多选题准确率，适合语言研究者
多选阅读理解（MCRC）模型旨在从一组候选选项中选择正确答案。然而，这些模型通常缺乏解释其选择依据的能力。本文提出一个全新的越南语数据集，用于训练和评估具备解释生成能力的MCRC模型。同时，我们设计了ViMultiChoice方法，专门针对越南语阅读理解任务，能够联合预测正确答案并生成相应解释。实验表明，ViMultiChoice在ViMMRC 2.0基准和新引入的数据集上均优于现有基线模型，达到当前最优（SotA）水平。此外，我们验证了联合训练选项决策与解释生成可显著提升多选题准确率。
原文摘要 · Abstract (English)
Multiple-choice Reading Comprehension (MCRC) models aim to select the correct answer from a set of candidate options for a given question. However, they typically lack the ability to explain the reasoning behind their choices. In this paper, we introduce a novel Vietnamese dataset designed to train and evaluate MCRC models with explanation generation capabilities. Furthermore, we propose ViMultiChoice, a new method specifically designed for modeling Vietnamese reading comprehension that jointly predicts the correct answer and generates a corresponding explanation. Experimental results demonstrate that ViMultiChoice outperforms existing MCRC baselines, achieving state-of-the-art (SotA) performance on both the ViMMRC 2.0 benchmark and the newly introduced dataset. Additionally, we show that jointly training option decision and explanation generation leads to significant improvements in multiple-choice accuracy.
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