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Large-scale and high-quality corpora are necessary for evaluating machine reading comprehension models on a low-resource language like Vietnamese. Besides, machine reading comprehension (MRC) for the health domain offers great potential for practical applications; however, there is still very little MRC research in this domain. This paper presents ViNewsQA as a new corpus for the Vietnamese language to evaluate healthcare reading comprehension models. The corpus comprises 22,057 human-generated question-answer pairs. Crowd-workers create the questions and their answers based on a collection of over 4,416 online Vietnamese healthcare news articles, where the answers comprise spans extracted from the corresponding articles. In particular, we develop a process of creating a corpus for the Vietnamese machine reading comprehension. Comprehensive evaluations demonstrate that our corpus requires abilities beyond simple reasoning, such as word matching and demanding difficult reasoning based on single-or-multiple-sentence information. We conduct experiments using different types of machine reading comprehension methods to achieve the first baseline performances, compared with further models performances. We also measure human performance on the corpus and compared it with several powerful neural network-based and transfer learning-based models. Our experiments show that the best machine model is ALBERT, which achieves an exact match score of 65.26% and an F1-score of 84.89% on our corpus. The significant differences between humans and the best-performance model (14.53% of EM and 10.90% of F1-score) on the test set of our corpus indicate that improvements in ViNewsQA could be explored in the future study. Our corpus is publicly available on our website for the research purpose to encourage the research community to make these improvements.
The development of natural language processing (NLP) in general and machine reading comprehension in particular has attracted the great attention of the research community. In recent years, there are a few datasets for machine reading comprehension t
Over 97 million people speak Vietnamese as their native language in the world. However, there are few research studies on machine reading comprehension (MRC) for Vietnamese, the task of understanding a text and answering questions related to it. Due
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 re
Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks. However, it is still difficult to interpret the predictions of existing MRC models. In this paper, we focus on extracting evidence s
In this paper, we focus on unsupervised domain adaptation for Machine Reading Comprehension (MRC), where the source domain has a large amount of labeled data, while only unlabeled passages are available in the target domain. To this end, we propose a