  One of the emerging research trends in natural language understanding is
machine reading comprehension (MRC) which is the task to find answers to human
questions based on textual data. Existing Vietnamese datasets for MRC research
concentrate solely on answerable questions. However, in reality, questions can
be unanswerable for which the correct answer is not stated in the given textual
data. To address the weakness, we provide the research community with a
benchmark dataset named UIT-ViQuAD 2.0 for evaluating the MRC task and question
answering systems for the Vietnamese language. We use UIT-ViQuAD 2.0 as a
benchmark dataset for the challenge on Vietnamese MRC at the Eighth Workshop on
Vietnamese Language and Speech Processing (VLSP 2021). This task attracted 77
participant teams from 34 universities and other organizations. In this
article, we present details of the organization of the challenge, an overview
of the methods employed by shared-task participants, and the results. The
highest performances are 77.24% in F1-score and 67.43% in Exact Match on the
private test set. The Vietnamese MRC systems proposed by the top 3 teams use
XLM-RoBERTa, a powerful pre-trained language model based on the transformer
architecture. The UIT-ViQuAD 2.0 dataset motivates researchers to further
explore the Vietnamese machine reading comprehension task and related tasks
such as question answering, question generation, and natural language
inference.
