  Machine reading comprehension (MRC) is a challenging task in natural language
processing that makes computers understanding natural language texts and answer
questions based on those texts. There are many techniques for solving this
problems, and word representation is a very important technique that impact
most to the accuracy of machine reading comprehension problem in the popular
languages like English and Chinese. However, few studies on MRC have been
conducted in low-resource languages such as Vietnamese. In this paper, we
conduct several experiments on neural network-based model to understand the
impact of word representation to the Vietnamese multiple-choice machine reading
comprehension. Our experiments include using the Co-match model on six
different Vietnamese word embeddings and the BERT model for multiple-choice
reading comprehension. On the ViMMRC corpus, the accuracy of BERT model is
61.28% on test set.
