Machine Reading Comprehension (MRC) is the task of natural languageprocessing that studies the ability to read and understand unstructured texts and then find the correct answers for questions. Until now, we have not yet had any MRC dataset for such a low-resource language as Vietnamese. In this paper, we introduce ViMMRC, a challenging machine comprehension corpus with multiple-choice questions, intended for research on the machine comprehension of Vietnamesetext. This corpus includes 2,783  multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for 1st to 5th graders. Answers may be extracted from the contents of single or multiple sentences in the corresponding reading text. A thorough analysis of the corpus and experimental results in this paper illustrate that our corpus ViMMRC demands reasoning abilities beyond simple word matching. We proposed the method of Boosted Sliding Window (BSW) that improves 5.51\% in accuracy over the best baseline method. We also measured human performance on the corpus 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. The corpus is freely available at our website for research purposes.
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 models in 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.
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Machine Reading Comprehension (MRC) is the task of natural languageprocessing that studies the ability to read and understand unstructured texts and then find the correct answers for questions. Until now, we have not yet had any MRC dataset for such a low-resource language as Vietnamese. In this paper, we introduce ViMMRC, a challenging machine comprehension corpus with multiple-choice questions, intended for research on the machine comprehension of Vietnamesetext. This corpus includes
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
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Answers may be extracted from the contents of single or multiple sentences in the corresponding reading text. A thorough analysis of the corpus and experimental results in this paper illustrate that our corpus ViMMRC demands reasoning abilities beyond simple word matching. We proposed the method of Boosted Sliding Window (BSW) that improves
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 models in terms of accuracy of 61.81\%, higher
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