SAGA: A Participant-specific Examination of Story Alternatives and Goal Applicability for a Deeper Understanding of Complex Events

Sai Vallurupalli, Katrin Erk, Francis Ferraro

Abstract
Interpreting and assessing goal driven actions is vital to understanding and reasoning over complex events. It is important to be able to acquire the knowledge needed for this understanding, though doing so is challenging. We argue that such knowledge can be elicited through a participant achievement lens. We analyze a complex event in a narrative according to the intended achievements of the participants in that narrative, the likely future actions of the participants, and the likelihood of goal success. We collect 6.3K high quality goal and action annotations reflecting our proposed participant achievement lens, with an average weighted Fleiss-Kappa IAA of 80%. Our collection contains annotated alternate versions of each narrative. These alternate versions vary minimally from the “original” story, but can license drastically different inferences. Our findings suggest that while modern large language models can reflect some of the goal-based knowledge we study, they find it challenging to fully capture the design and intent behind concerted actions, even when the model pretraining included the data from which we extracted the goal knowledge. We show that smaller models fine-tuned on our dataset can achieve performance surpassing larger models.
Anthology ID:
2024.findings-acl.910
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
15396–15420
Language:
URL:
https://aclanthology.org/2024.findings-acl.910
DOI:
10.18653/v1/2024.findings-acl.910
Bibkey:
Cite (ACL):
Sai Vallurupalli, Katrin Erk, and Francis Ferraro. 2024. SAGA: A Participant-specific Examination of Story Alternatives and Goal Applicability for a Deeper Understanding of Complex Events. In Findings of the Association for Computational Linguistics: ACL 2024, pages 15396–15420, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
SAGA: A Participant-specific Examination of Story Alternatives and Goal Applicability for a Deeper Understanding of Complex Events (Vallurupalli et al., Findings 2024)
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PDF:
https://preview.aclanthology.org/autopr/2024.findings-acl.910.pdf
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