# Constructing Hierarchical Q&A Datasets for Video Story Understanding

arXiv: https://arxiv.org/abs/1904.00623 · HTML: https://mlsanity.com/paper/1904.00623/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

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

- [Adaptive Hierarchical Graph Reasoning with Semantic Coherence for Video-and-Language Inference](https://mlsanity.com/paper/2107.12270/) (2021)
- [CogME: A Cognition-Inspired Multi-Dimensional Evaluation Metric for Story Understanding](https://mlsanity.com/paper/2107.09847/) (2021)
- [Toward a Human-Level Video Understanding Intelligence](https://mlsanity.com/paper/2110.04203/) (2021)

## Abstract (arXiv)

Video understanding is emerging as a new paradigm for studying human-like AI. Question-and-Answering (Q&A) is used as a general benchmark to measure the level of intelligence for video understanding. While several previous studies have suggested datasets for video Q&A tasks, they did not really incorporate story-level understanding, resulting in highly-biased and lack of variance in degree of question difficulty. In this paper, we propose a hierarchical method for building Q&A datasets, i.e. hierarchical difficulty levels. We introduce three criteria for video story understanding, i.e. memory capacity, logical complexity, and DIKW (Data-Information-Knowledge-Wisdom) pyramid. We discuss how three-dimensional map constructed from these criteria can be used as a metric for evaluating the levels of intelligence relating to video story understanding.