  Question answering (QA) is a natural language understanding task within the
fields of information retrieval and information extraction that has attracted
much attention from the computational linguistics and artificial intelligence
research community in recent years because of the strong development of machine
reading comprehension-based models. A reader-based QA system is a high-level
search engine that can find correct answers to queries or questions in
open-domain or domain-specific texts using machine reading comprehension (MRC)
techniques. The majority of advancements in data resources and machine-learning
approaches in the MRC and QA systems especially are developed significantly in
two resource-rich languages such as English and Chinese. A low-resource
language like Vietnamese has witnessed a scarcity of research on QA systems.
This paper presents XLMRQA, the first Vietnamese QA system using a supervised
transformer-based reader on the Wikipedia-based textual knowledge source (using
the UIT-ViQuAD corpus), outperforming the two robust QA systems using deep
neural network models: DrQA and BERTserini with 24.46% and 6.28%, respectively.
From the results obtained on the three systems, we analyze the influence of
question types on the performance of the QA systems.
