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arXiv · 2602.09961

ViMultiChoice: Toward a Method That Gives Explanation for Multiple-Choice Reading Comprehension in Vietnamese

Trung Tien Cao·Lam Minh Thai·Nghia Hieu Nguyen·Duc-Vu Nguyen·Ngan Luu-Thuy Nguyen
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Abstract

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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Trung Tien Cao, Lam Minh Thai, Nghia Hieu Nguyen, Duc-Vu Nguyen, Ngan Luu-Thuy Nguyen. 2026-02-10. ViMultiChoice: Toward a Method That Gives Explanation for Multiple-Choice Reading Comprehension in Vietnamese. https://arxiv.org/abs/2602.09961

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