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 pairs of multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for 1st to 5th graders. The objectives of this research are two main contributions: (1) to develop a human-generated benchmark dataset for the low-resourced Vietnamese language for the machine reading comprehension; and (2) to evaluate machine reading comprehension techniques using lexical-based approaches, neural-based approaches, and our proposed method. Finally, the results of our proposed model are analyzed by comparing it with the outcome of the lexical-based and neural-based approaches. Our experiments show that our proposed method outperforms baseline modelsin terms of accuracy of 61.81 \%, higher 5.51\% than the best baseline model. Besides, we measure human performance on our dataset and compared it to our MRC models. The performance gap between humans and our best experimental model indicates that significant progress can be made on Vietnamese machine reading comprehension in further research. Our dataset is freely available for research purposes. \\%DIF > 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.
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 \\%DIF > 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.
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limited study and efforts have been made to carry out
there are not many research studies on Vietnamese
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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
(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
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based on a set of 417 Vietnamese texts
. The texts are commonly
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1st to 5th graders. The objectives of this research are two main contributions: (1) to develop a human-generated benchmark dataset for the low-resourced Vietnamese language for the machine reading comprehension; and (2) to evaluate machine reading comprehension techniques using
elementary school pupils. In addition, we propose a
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approaches, neural-based approaches, and our proposed method. Finally, the results of our proposed model are analyzed by comparing it with the outcome of the
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
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neural-based approaches. Our experiments show that our proposed method outperforms baseline modelsin terms of accuracy of
neural network-based baseline models. Our proposed technique achieves
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\%, higher 5.51\% than the best baseline model. Besides, we measure human performance on our dataset and compared it to our MRC models. The performance gap between humans and our best experimental model indicates that significant progress can be made on Vietnamese machine reading comprehension in further research. Our dataset is freely available for research purposes.