A Survey of Machine Narrative Reading Comprehension Assessments

Yisi Sang; Xiangyang Mou; Jing Li; Jeffrey Stanton; Mo Yu

IJCAI 2022poster16 citations

A Survey of Machine Narrative Reading Comprehension Assessments

Yisi Sang, Xiangyang Mou, Jing Li, Jeffrey Stanton, Mo Yu

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.

Survey Track: Natural Language Processing

BibTeX

@inproceedings{ijcai2022p779,
  title     = {A Survey of Machine Narrative Reading Comprehension Assessments},
  author    = {Sang, Yisi and Mou, Xiangyang and Li, Jing and Stanton, Jeffrey and Yu, Mo},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {5580--5587},
  year      = {2022},
  month     = {7},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2022/779},
  url       = {https://doi.org/10.24963/ijcai.2022/779},
}