Abstract
Stance detection is an increasingly popular task that has been mainly modeled as a static task, by assigning the expressed attitude of a text toward a given topic. Such a framing presents limitations, with trained systems showing poor generalization capabilities and being strongly topic-dependent. In this work, we propose modeling stance as a dynamic task, by focusing on the interactions between a message and their replies. For this purpose, we present a new annotation scheme that enables the categorization of all kinds of textual interactions. As a result, we have created a new corpus, the Dynamic Stance Corpus (DySC), consisting of three datasets in two middle-resourced languages: Catalan and Dutch. Our data analysis further supports our modeling decisions, empirically showing differences between the annotation of stance in static and dynamic contexts. We fine-tuned a series of monolingual and multilingual models on DySC, showing portability across topics and languages.- Anthology ID:
- 2023.findings-emnlp.432
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6503–6515
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.432
- DOI:
- 10.18653/v1/2023.findings-emnlp.432
- Cite (ACL):
- Blanca Figueras, Irene Baucells, and Tommaso Caselli. 2023. Dynamic Stance: Modeling Discussions by Labeling the Interactions. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6503–6515, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Dynamic Stance: Modeling Discussions by Labeling the Interactions (Figueras et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2023.findings-emnlp.432.pdf