Before revision: Machine Reading Comprehension (MRC) is the task of natural languageprocessing that studies the ability to read and understand unstructured texts and then find the correct answers for questions. Until now, we have not yet had any MRC dataset for such a low-resource language as Vietnamese. In this paper, we introduce ViMMRC, a challenging machine comprehension corpus with multiple-choice questions, intended for research on the machine comprehension of Vietnamesetext. This corpus includes 2,783  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 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 multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for 1st to 5th graders.