TutorialVQA: Question Answering Dataset for Tutorial Videos

Anthony Colas, Seokhwan Kim, Franck Dernoncourt, Siddhesh Gupte, Zhe Wang, Doo Soon Kim


Abstract
Despite the number of currently available datasets on video-question answering, there still remains a need for a dataset involving multi-step and non-factoid answers. Moreover, relying on video transcripts remains an under-explored topic. To adequately address this, we propose a new question answering task on instructional videos, because of their verbose and narrative nature. While previous studies on video question answering have focused on generating a short text as an answer, given a question and video clip, our task aims to identify a span of a video segment as an answer which contains instructional details with various granularities. This work focuses on screencast tutorial videos pertaining to an image editing program. We introduce a dataset, TutorialVQA, consisting of about 6,000 manually collected triples of (video, question, answer span). We also provide experimental results with several baseline algorithms using the video transcripts. The results indicate that the task is challenging and call for the investigation of new algorithms.
Anthology ID:
2020.lrec-1.670
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
5450–5455
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.670
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.670.pdf