On Narrative Question Answering Skills

Emil Kalbaliyev, Kairit Sirts


Abstract
Narrative Question Answering is an important task for evaluating and improving reading comprehension abilities in both humans and machines. However, there is a lack of consensus on the skill taxonomy that would enable systematic and comprehensive assessment and learning of the various aspects of Narrative Question Answering. Existing task-level skill views oversimplify the multidimensional nature of tasks, while question-level taxonomies face issues in evaluation and methodology. To address these challenges, we introduce a more inclusive skill taxonomy that synthesizes and redefines narrative understanding skills from previous taxonomies and includes a generation skill dimension from the answering perspective.
Anthology ID:
2024.naacl-short.73
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
814–820
Language:
URL:
https://aclanthology.org/2024.naacl-short.73
DOI:
10.18653/v1/2024.naacl-short.73
Bibkey:
Cite (ACL):
Emil Kalbaliyev and Kairit Sirts. 2024. On Narrative Question Answering Skills. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 814–820, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
On Narrative Question Answering Skills (Kalbaliyev & Sirts, NAACL 2024)
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PDF:
https://aclanthology.org/2024.naacl-short.73.pdf