A Survey of Machine Narrative Reading Comprehension Assessments
Artificial Intelligence
2022-05-03 v1
Abstract
As the body of research on machine narrative comprehension grows, there is a critical need for consideration of performance assessment strategies as well as the depth and scope of different benchmark tasks. Based on narrative theories, reading comprehension theories, as well as existing machine narrative reading comprehension tasks and datasets, we propose a typology that captures the main similarities and differences among assessment tasks; and discuss the implications of our typology for new task design and the challenges of narrative reading comprehension.
Keywords
Cite
@article{arxiv.2205.00299,
title = {A Survey of Machine Narrative Reading Comprehension Assessments},
author = {Yisi Sang and Xiangyang Mou and Jing Li and Jeffrey Stanton and Mo Yu},
journal= {arXiv preprint arXiv:2205.00299},
year = {2022}
}
Comments
accepted for the IJCAI-ECAI2022 Survey Track