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        <title>Dynamic Stance: Modeling Discussions by Labeling the Interactions</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Dynamic Stance: Modeling Discussions by Labeling the Interactions
%A Figueras, Blanca
%A Baucells, Irene
%A Caselli, Tommaso
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F figueras-etal-2023-dynamic
%X 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.
%R 10.18653/v1/2023.findings-emnlp.432
%U https://aclanthology.org/2023.findings-emnlp.432/
%U https://doi.org/10.18653/v1/2023.findings-emnlp.432
%P 6503-6515

Markdown (Informal)

[Dynamic Stance: Modeling Discussions by Labeling the Interactions](https://aclanthology.org/2023.findings-emnlp.432/) (Figueras et al., Findings 2023)

• Dynamic Stance: Modeling Discussions by Labeling the Interactions (Figueras et al., Findings 2023)

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.