YTCommentQA: Video Question Answerability in Instructional Videos
Saelyne Yang Sunghyun Park Yunseok Jang Moontae Lee
Instructional videos provide detailed how-to guides for various tasks, with
viewers often posing questions regarding the content. Addressing these
questions is vital for comprehending the content, yet receiving immediate
answers is difficult. While numerous computational models have been developed
for Video Question Answering (Video QA) tasks, they are primarily trained on
questions generated based on video content, aiming to produce answers from
within the content. However, in real-world situations, users may pose questions
that go beyond the video's informational boundaries, highlighting the necessity
to determine if a video can provide the answer. Discerning whether a question
can be answered by video content is challenging due to the multi-modal nature
of videos, where visual and verbal information are intertwined. To bridge this
gap, we present the YTCommentQA dataset, which contains naturally-generated
questions from YouTube, categorized by their answerability and required
modality to answer -- visual, script, or both. Experiments with answerability
classification tasks demonstrate the complexity of YTCommentQA and emphasize
the need to comprehend the combined role of visual and script information in
video reasoning. The dataset is available at
https://github.com/lgresearch/YTCommentQA.