# YTCommentQA: Video Question Answerability in Instructional Videos

arXiv: https://arxiv.org/abs/2401.17343 · HTML: https://mlsanity.com/paper/2401.17343/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 46 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 24 of 24)

- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [VizWiz Grand Challenge: Answering Visual Questions from Blind People](https://mlsanity.com/paper/1802.08218/) (2018)
- [NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions](https://mlsanity.com/paper/2105.08276/) (2021)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering](https://mlsanity.com/paper/1906.02467/) (2019)
- [TVQA: Localized, Compositional Video Question Answering](https://mlsanity.com/paper/1809.01696/) (2018)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training](https://mlsanity.com/paper/2005.00200/) (2020)
- [MERLOT: Multimodal Neural Script Knowledge Models](https://mlsanity.com/paper/2106.02636/) (2021)
- [MovieQA: Understanding Stories in Movies through Question-Answering](https://mlsanity.com/paper/1512.02902/) (2015)
- [Just Ask: Learning to Answer Questions from Millions of Narrated Videos](https://mlsanity.com/paper/2012.00451/) (2020)
- [Zero-Shot Video Question Answering via Frozen Bidirectional Language Models](https://mlsanity.com/paper/2206.08155/) (2022)
- [AGQA: A Benchmark for Compositional Spatio-Temporal Reasoning](https://mlsanity.com/paper/2103.16002/) (2021)
- [Video Question Answering: Datasets, Algorithms and Challenges](https://mlsanity.com/paper/2203.01225/) (2022)
- [KnowIT VQA: Answering Knowledge-Based Questions about Videos](https://mlsanity.com/paper/1910.10706/) (2019)
- [Video Question Answering via Attribute-Augmented Attention Network Learning](https://mlsanity.com/paper/1707.06355/) (2017)
- [Read + Verify: Machine Reading Comprehension with Unanswerable Questions](https://mlsanity.com/paper/1808.05759/) (2018)
- [TutorialVQA: Question Answering Dataset for Tutorial Videos](https://mlsanity.com/paper/1912.01046/) (2019)
- [Question Relevance in VQA: Identifying Non-Visual And False-Premise Questions](https://mlsanity.com/paper/1606.06622/) (2016)
- [The Promise of Premise: Harnessing Question Premises in Visual Question Answering](https://mlsanity.com/paper/1705.00601/) (2017)
- [Video Question Answering on Screencast Tutorials](https://mlsanity.com/paper/2008.00544/) (2020)
- [Towards Interpretable and Reliable Reading Comprehension: A Pipeline Model with Unanswerability Prediction](https://mlsanity.com/paper/2111.09029/) (2021)

## Built on by (most cited first, 3 of 3)

- [VideoMix: Aggregating How-To Videos for Task-Oriented Learning](https://mlsanity.com/paper/2503.21130/) (2025)
- [EduVidQA: Generating and Evaluating Long-form Answers to Student Questions based on Lecture Videos](https://mlsanity.com/paper/2509.24120/) (2025)
- [STORM-Bench: Evaluating Online Video QA under Evolving and Incomplete Evidence](https://mlsanity.com/paper/2609.30981/) (2026)

## Abstract (arXiv)

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.