# TutorialVQA: Question Answering Dataset for Tutorial Videos

arXiv: https://arxiv.org/abs/1912.01046 · HTML: https://mlsanity.com/paper/1912.01046/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 20 of 20)

- [Zero-Shot Video Question Answering via Frozen Bidirectional Language Models](https://mlsanity.com/paper/2206.08155/) (2022)
- [Document Understanding Dataset and Evaluation (DUDE)](https://mlsanity.com/paper/2305.08455/) (2023)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [Video Question Answering: Datasets, Algorithms and Challenges](https://mlsanity.com/paper/2203.01225/) (2022)
- [Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Video](https://mlsanity.com/paper/2203.06667/) (2022)
- [Video-Thinker: Sparking "Thinking with Videos" via Reinforcement Learning](https://mlsanity.com/paper/2510.23473/) (2025)
- [Learning to Answer Visual Questions from Web Videos](https://mlsanity.com/paper/2205.05019/) (2022)
- [Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer](https://mlsanity.com/paper/2308.08414/) (2023)
- [WildQA: In-the-Wild Video Question Answering](https://mlsanity.com/paper/2209.06650/) (2022)
- [AssistSR: Task-oriented Video Segment Retrieval for Personal AI Assistant](https://mlsanity.com/paper/2111.15050/) (2021)
- [Visual Answer Localization with Cross-modal Mutual Knowledge Transfer](https://mlsanity.com/paper/2210.14823/) (2022)
- [D'ej`a Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse](https://mlsanity.com/paper/2506.14107/) (2025)
- [Music Audio-Visual Question Answering Requires Specialized Multimodal Designs](https://mlsanity.com/paper/2505.20638/) (2025)
- [YTCommentQA: Video Question Answerability in Instructional Videos](https://mlsanity.com/paper/2401.17343/) (2024)
- [EduVidQA: Generating and Evaluating Long-form Answers to Student Questions based on Lecture Videos](https://mlsanity.com/paper/2509.24120/) (2025)
- [Human in the loop approaches in multi-modal conversational task guidance system development](https://mlsanity.com/paper/2211.01824/) (2022)
- [Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational Videos](https://mlsanity.com/paper/2505.01790/) (2025)
- [QTG-VQA: Question-Type-Guided Architectural for VideoQA Systems](https://mlsanity.com/paper/2409.09348/) (2024)
- [VideoChain: A Transformer-Based Framework for Multi-hop Video Question Generation](https://mlsanity.com/paper/2511.08348/) (2025)
- [CACR:Reinforcing Temporal Answer Grounding in Instructional Video via Candidate-Aware Causal Reasoning](https://mlsanity.com/paper/2606.08436/) (2026)

## Abstract (arXiv)

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.