  Although Vietnamese is the 17th most popular native-speaker language in the
world, there are not many research studies on Vietnamese 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
2,783 pairs of multiple-choice questions and answers based on 417 Vietnamese
texts which are commonly used for teaching reading comprehension for elementary
school pupils. In addition, we propose a lexical-based MRC method that utilizes
semantic similarity measures and external knowledge sources to analyze
questions and extract answers from the given text. We compare the performance
of the proposed model with several baseline lexical-based and neural
network-based models. Our proposed method achieves 61.81% by accuracy, which is
5.51% higher than the best baseline model. We also measure human performance on
our dataset and find that there is a big gap between machine-model and human
performances. This indicates that significant progress can be made on this
task. The dataset is freely available on our website for research purposes.
