Before revision: Although over 95 million people worldwide speak the Vietnamese language, limited study and efforts have been made to carry out machine reading comprehension and create language resources for the Vietnamese language. This article proposes the lexical-based reading comprehension approach utilizing semantic similarity measurement and external knowledge sources to analyze questions and extract answers from reading texts in Vietnamese. This method is evaluated on our proposed dataset including 2,783 pairs of multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for 1st to 5th graders.
After revision: Although over 95 million people worldwide speak the Vietnamese language, limited study and efforts have been made to carry out machine reading comprehension (MRC), the task of understanding a text and answering questions about it. One of the reasons is because of the lack of high-quality benchmark datasets for this task. In this work, we construct a dataset which consists of 417 Vietnamese texts and 2,783 pairs of multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for 1st to 5th graders.