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

TL;DR

This paper introduces ViMultiChoice, a novel Vietnamese dataset and method for multiple-choice reading comprehension that jointly predicts answers and generates explanations, achieving state-of-the-art results and improving interpretability.

Contribution

The paper presents a new Vietnamese dataset and a joint answer-explanation model, advancing MCRC with explainability and superior performance.

Findings

ViMultiChoice outperforms existing baselines on ViMMRC 2.0.

Joint training improves multiple-choice accuracy.

The method enhances interpretability in Vietnamese MCRC.

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.