@inproceedings{barel-etal-2025-acquired,
title = "Acquired {TASTE}: Multimodal Stance Detection with Textual and Structural Embeddings",
author = "Barel, Guy and
Tsur, Oren and
Vilenchik, Dan",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.433/",
pages = "6492--6504",
abstract = "Stance detection plays a pivotal role in enabling an extensive range of downstream applications, from discourse parsing to tracing the spread of fake news and the denial of scientific facts. While most stance classification models rely on the textual representation of the utterance in question, prior work has demonstrated the importance of the conversational context in stance detection. In this work, we introduce TASTE {--} a multimodal architecture for stance detection that harmoniously fuses Transformer-based content embedding with unsupervised structural embedding. Through the fine-tuning of a pre-trained transformer and the amalgamation with social embedding via a Gated Residual Network (GRN) layer, our model adeptly captures the complex interplay between content and conversational structure in determining stance. TASTE achieves state-of-the-art results on common benchmarks, significantly outperforming an array of strong baselines. Comparative evaluations underscore the benefits of social grounding {--} emphasizing the criticality of concurrently harnessing both content and structure for enhanced stance detection."
}
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<abstract>Stance detection plays a pivotal role in enabling an extensive range of downstream applications, from discourse parsing to tracing the spread of fake news and the denial of scientific facts. While most stance classification models rely on the textual representation of the utterance in question, prior work has demonstrated the importance of the conversational context in stance detection. In this work, we introduce TASTE – a multimodal architecture for stance detection that harmoniously fuses Transformer-based content embedding with unsupervised structural embedding. Through the fine-tuning of a pre-trained transformer and the amalgamation with social embedding via a Gated Residual Network (GRN) layer, our model adeptly captures the complex interplay between content and conversational structure in determining stance. TASTE achieves state-of-the-art results on common benchmarks, significantly outperforming an array of strong baselines. Comparative evaluations underscore the benefits of social grounding – emphasizing the criticality of concurrently harnessing both content and structure for enhanced stance detection.</abstract>
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%0 Conference Proceedings
%T Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings
%A Barel, Guy
%A Tsur, Oren
%A Vilenchik, Dan
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F barel-etal-2025-acquired
%X Stance detection plays a pivotal role in enabling an extensive range of downstream applications, from discourse parsing to tracing the spread of fake news and the denial of scientific facts. While most stance classification models rely on the textual representation of the utterance in question, prior work has demonstrated the importance of the conversational context in stance detection. In this work, we introduce TASTE – a multimodal architecture for stance detection that harmoniously fuses Transformer-based content embedding with unsupervised structural embedding. Through the fine-tuning of a pre-trained transformer and the amalgamation with social embedding via a Gated Residual Network (GRN) layer, our model adeptly captures the complex interplay between content and conversational structure in determining stance. TASTE achieves state-of-the-art results on common benchmarks, significantly outperforming an array of strong baselines. Comparative evaluations underscore the benefits of social grounding – emphasizing the criticality of concurrently harnessing both content and structure for enhanced stance detection.
%U https://aclanthology.org/2025.coling-main.433/
%P 6492-6504
Markdown (Informal)
[Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings](https://aclanthology.org/2025.coling-main.433/) (Barel et al., COLING 2025)
ACL