  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 6,000manually collected triples of
(video, question, answer span). We also provide experimental results with
several baselines algorithms using the video transcripts. The results indicate
that the task is challenging and call for the investigation of new algorithms.
