  This paper presents the development process of a Vietnamese spoken language
corpus for machine reading comprehension (MRC) tasks and provides insights into
the challenges and opportunities associated with using real-world data for
machine reading comprehension tasks. The existing MRC corpora in Vietnamese
mainly focus on formal written documents such as Wikipedia articles, online
newspapers, or textbooks. In contrast, the VlogQA consists of 10,076
question-answer pairs based on 1,230 transcript documents sourced from YouTube
-- an extensive source of user-uploaded content, covering the topics of food
and travel. By capturing the spoken language of native Vietnamese speakers in
natural settings, an obscure corner overlooked in Vietnamese research, the
corpus provides a valuable resource for future research in reading
comprehension tasks for the Vietnamese language. Regarding performance
evaluation, our deep-learning models achieved the highest F1 score of 75.34% on
the test set, indicating significant progress in machine reading comprehension
for Vietnamese spoken language data. In terms of EM, the highest score we
accomplished is 53.97%, which reflects the challenge in processing spoken-based
content and highlights the need for further improvement.
