Scaling up Discourse Quality Annotation for Political Science

Neele Falk, Gabriella Lapesa


Abstract
The empirical quantification of the quality of a contribution to a political discussion is at the heart of deliberative theory, the subdiscipline of political science which investigates decision-making in deliberative democracy. Existing annotation on deliberative quality is time-consuming and carried out by experts, typically resulting in small datasets which also suffer from strong class imbalance. Scaling up such annotations with automatic tools is desirable, but very challenging. We take up this challenge and explore different strategies to improve the prediction of deliberative quality dimensions (justification, common good, interactivity, respect) in a standard dataset. Our results show that simple data augmentation techniques successfully alleviate data imbalance. Classifiers based on linguistic features (textual complexity and sentiment/polarity) and classifiers integrating argument quality annotations (from the argument mining community in NLP) were consistently outperformed by transformer-based models, with or without data augmentation.
Anthology ID:
2022.lrec-1.353
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
3301–3318
Language:
URL:
https://aclanthology.org/2022.lrec-1.353
DOI:
Bibkey:
Cite (ACL):
Neele Falk and Gabriella Lapesa. 2022. Scaling up Discourse Quality Annotation for Political Science. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3301–3318, Marseille, France. European Language Resources Association.
Cite (Informal):
Scaling up Discourse Quality Annotation for Political Science (Falk & Lapesa, LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.353.pdf
Code
 blubberli/empiricaldqi