Do Dependency Relations Help in the Task of Stance Detection?

Alessandra Teresa Cignarella, Cristina Bosco, Paolo Rosso


Abstract
In this paper we present a set of multilingual experiments tackling the task of Stance Detection in five different languages: English, Spanish, Catalan, French and Italian. Furthermore, we study the phenomenon of stance with respect to six different targets – one per language, and two different for Italian – employing a variety of machine learning algorithms that primarily exploit morphological and syntactic knowledge as features, represented throughout the format of Universal Dependencies. Results seem to suggest that the methodology employed is not beneficial per se, but might be useful to exploit the same features with a different methodology.
Anthology ID:
2022.insights-1.2
Volume:
Proceedings of the Third Workshop on Insights from Negative Results in NLP
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Shabnam Tafreshi, João Sedoc, Anna Rogers, Aleksandr Drozd, Anna Rumshisky, Arjun Akula
Venue:
insights
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10–17
Language:
URL:
https://aclanthology.org/2022.insights-1.2
DOI:
10.18653/v1/2022.insights-1.2
Bibkey:
Cite (ACL):
Alessandra Teresa Cignarella, Cristina Bosco, and Paolo Rosso. 2022. Do Dependency Relations Help in the Task of Stance Detection?. In Proceedings of the Third Workshop on Insights from Negative Results in NLP, pages 10–17, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Do Dependency Relations Help in the Task of Stance Detection? (Cignarella et al., insights 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.insights-1.2.pdf
Video:
 https://aclanthology.org/2022.insights-1.2.mp4