before_revision: Although over 95 million people worldwide speak the Vietnamese language , 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 417 Vietnamese texts and 2,783 pairs of multiple-choice questions and answers . The texts are commonly used for teaching reading comprehension for elementary school pupils. In addition, we propose a lexical-based MRC technique 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  lexical-based and neural network-based baseline models. Our proposed technique achieves 61.81\% in 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 human and model performances. This indicates that significant progress can be made on this task. The dataset is freely available at our website for research purposes.
after_revision: 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.
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