English

CogME: A Cognition-Inspired Multi-Dimensional Evaluation Metric for Story Understanding

Computer Vision and Pattern Recognition 2024-05-21 v3 Artificial Intelligence

Abstract

We introduce CogME, a cognition-inspired, multi-dimensional evaluation metric designed for AI models focusing on story understanding. CogME is a framework grounded in human thinking strategies and story elements that involve story understanding. With a specific breakdown of the questions, this approach provides a nuanced assessment revealing not only AI models' particular strengths and weaknesses but also the characteristics of the benchmark dataset. Our case study with the DramaQA dataset demonstrates a refined analysis of the model and the benchmark dataset. We argue the need for metrics based on understanding the nature of tasks and designed to align closely with human cognitive processes. This approach provides insights beyond traditional overall scores and paves the way for more sophisticated AI development targeting higher cognitive functions.

Keywords

Cite

@article{arxiv.2107.09847,
  title  = {CogME: A Cognition-Inspired Multi-Dimensional Evaluation Metric for Story Understanding},
  author = {Minjung Shin and Seongho Choi and Yu-Jung Heo and Minsu Lee and Byoung-Tak Zhang and Jeh-Kwang Ryu},
  journal= {arXiv preprint arXiv:2107.09847},
  year   = {2024}
}

Comments

9 pages with 4 figures and 3 tables. This work has been accepted for presentation as a poster with full paper publication at CogSci 2024. This is the final submission