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.

本文介绍了利用多个神经网络模型对低资源语言（越南语）中的多项选择机器阅读理解的影响进行的实验，其中使用了六种不同的越南语单词嵌入和BERT模型，在ViMMRC语料库上的测试集中，BERT模型的准确率为61.28％。

越南多项选择阅读理解的深度神经网络模型实验研究