Machine reading comprehension is a natural language understanding task where the computing system is required to read a text and then find the answer to a specific question posed by a human. Large-scale and high-quality corpora are necessary for evaluating machine reading comprehension models. Furthermore, machine reading comprehension (MRC) for the health sector has potential for practical applications; nevertheless, MRC research in this domain is currently scarce. This article presents UIT-ViNewsQA, a new corpus for the Vietnamese language to evaluate MRC models for the healthcare textual domain. The corpus consists of 22,057 human-generated question-answer pairs. Crowd-workers create the questions and answers on a collection of 4,416 online Vietnamese healthcare news articles, where the answers are textual spans extracted from the corresponding articles. We introduce a process for creating a high-quality corpus for the Vietnamese machine reading comprehension task. Linguistically, our corpus accommodates diversity in question and answer types. In addition, we conduct experiments and compare the effectiveness of different MRC methods based on the neural networks and transformer architectures. Experimental results on our corpus show that the MRC system based on ALBERT architecture outperforms the neural network architectures and the BERT-based approach, an exact match score of 65.26% and an F1-score of 84.89%. The best machine model achieves about 10.90% F1-score less efficiently than humans, which proves that exploring machine models on UIT-ViNewsQA to surpass humans is challenging for researchers in the future. Our corpus is publicly available on our website: http://nlp.uit.edu.vn/datasets for research purposes.

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 to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset (UIT-ViQuAD), a new dataset for the low-resource language as Vietnamese to evaluate MRC models.This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia.In particular, we propose a new process of dataset creation for Vietnamese MRC.Our in-depth analyses illustrate that our dataset requires abilities beyond simple reasoning like word matching and demands single-sentence and multiple-sentence inferences.Besides, we conduct experiments on state-of-the-art MRC methods for English and Chinese as the first experimental models on UIT-ViQuAD.We also estimate human performance on the dataset and compare it to the experimental results of powerful machine learning models.As a result, the substantial differences between human performance and the best model performance on the dataset indicate that improvements can be made on UIT-ViQuAD in future research.Our dataset is freely available on our website 1 to encourage the research community to overcome challenges in Vietnamese MRC.