Before revision:  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.
After revision:  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\% in accuracy over the best baseline method.