Reports of personal experiences and stories in argumentation: datasets and analysis

Neele Falk, Gabriella Lapesa


Abstract
Reports of personal experiences or stories can play a crucial role in argumentation, as they represent an immediate and (often) relatable way to back up one’s position with respect to a given topic. They are easy to understand and increase empathy: this makes them powerful in argumentation. The impact of personal reports and stories in argumentation has been studied in the Social Sciences, but it is still largely underexplored in NLP. Our work is the first step towards filling this gap: our goal is to develop robust classifiers to identify documents containing personal experiences and reports. The main challenge is the scarcity of annotated data: our solution is to leverage existing annotations to be able to scale-up the analysis. Our contribution is two-fold. First, we conduct a set of in-domain and cross-domain experiments involving three datasets (two from Argument Mining, one from the Social Sciences), modeling architectures, training setups and fine-tuning options tailored to the involved domains. We show that despite the differences among datasets and annotations, robust cross-domain classification is possible. Second, we employ linear regression for performance mining, identifying performance trends both for overall classification performance and individual classifier predictions.
Anthology ID:
2022.acl-long.379
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5530–5553
Language:
URL:
https://aclanthology.org/2022.acl-long.379
DOI:
10.18653/v1/2022.acl-long.379
Bibkey:
Cite (ACL):
Neele Falk and Gabriella Lapesa. 2022. Reports of personal experiences and stories in argumentation: datasets and analysis. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5530–5553, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Reports of personal experiences and stories in argumentation: datasets and analysis (Falk & Lapesa, ACL 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl24-info/2022.acl-long.379.pdf
Video:
 https://preview.aclanthology.org/naacl24-info/2022.acl-long.379.mp4
Code
 blubberli/storytestimony