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.

翻译：多项选择阅读理解（MCRC）模型旨在从一组候选选项中为给定问题选择正确答案。然而，它们通常缺乏解释其选择背后推理过程的能力。本文介绍了一个新颖的越南语数据集，旨在训练和评估具备解释生成能力的MCRC模型。此外，我们提出了ViMultiChoice，这是一种专门为越南语阅读理解建模设计的新方法，它联合预测正确答案并生成相应的解释。实验结果表明，ViMultiChoice在现有MCRC基线模型上表现更优，在ViMMRC 2.0基准测试和新引入的数据集上均达到了最先进的性能。此外，我们还证明，联合训练选项决策和解释生成能显著提高多项选择的准确率。