@inproceedings{vasquez-etal-2025-gattention,
title = "{GA}ttention: Gated Attention for the Detection of Abusive Language",
author = "V{\'a}squez, Horacio Jarqu{\'i}n and
Escalante, Hugo Jair and
Montes, Manuel and
Aragon, Mario Ezra",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1105/",
doi = "10.18653/v1/2025.findings-emnlp.1105",
pages = "20314--20329",
ISBN = "979-8-89176-335-7",
abstract = "Abusive language online creates toxic environments and exacerbates social tensions, underscoring the need for robust NLP models to interpret nuanced linguistic cues. This paper introduces GAttention, a novel Gated Attention mechanism that combines the strengths of Contextual attention and Self-attention mechanisms to address the limitations of existing attention models within the text classification task. GAttention capitalizes on local and global query vectors by integrating the internal relationships within a sequence (Self-attention) and the global relationships among distinct sequences (Contextual attention). This combination allows for a more nuanced understanding and processing of sequence elements, which is particularly beneficial in context-sensitive text classification tasks such as the case of abusive language detection. By applying this mechanism to transformer-based encoder models, we showcase how it enhances the model{'}s ability to discern subtle nuances and contextual clues essential for identifying abusive language, a challenging and increasingly relevant NLP task."
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<abstract>Abusive language online creates toxic environments and exacerbates social tensions, underscoring the need for robust NLP models to interpret nuanced linguistic cues. This paper introduces GAttention, a novel Gated Attention mechanism that combines the strengths of Contextual attention and Self-attention mechanisms to address the limitations of existing attention models within the text classification task. GAttention capitalizes on local and global query vectors by integrating the internal relationships within a sequence (Self-attention) and the global relationships among distinct sequences (Contextual attention). This combination allows for a more nuanced understanding and processing of sequence elements, which is particularly beneficial in context-sensitive text classification tasks such as the case of abusive language detection. By applying this mechanism to transformer-based encoder models, we showcase how it enhances the model’s ability to discern subtle nuances and contextual clues essential for identifying abusive language, a challenging and increasingly relevant NLP task.</abstract>
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%0 Conference Proceedings
%T GAttention: Gated Attention for the Detection of Abusive Language
%A Vásquez, Horacio Jarquín
%A Escalante, Hugo Jair
%A Montes, Manuel
%A Aragon, Mario Ezra
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F vasquez-etal-2025-gattention
%X Abusive language online creates toxic environments and exacerbates social tensions, underscoring the need for robust NLP models to interpret nuanced linguistic cues. This paper introduces GAttention, a novel Gated Attention mechanism that combines the strengths of Contextual attention and Self-attention mechanisms to address the limitations of existing attention models within the text classification task. GAttention capitalizes on local and global query vectors by integrating the internal relationships within a sequence (Self-attention) and the global relationships among distinct sequences (Contextual attention). This combination allows for a more nuanced understanding and processing of sequence elements, which is particularly beneficial in context-sensitive text classification tasks such as the case of abusive language detection. By applying this mechanism to transformer-based encoder models, we showcase how it enhances the model’s ability to discern subtle nuances and contextual clues essential for identifying abusive language, a challenging and increasingly relevant NLP task.
%R 10.18653/v1/2025.findings-emnlp.1105
%U https://aclanthology.org/2025.findings-emnlp.1105/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1105
%P 20314-20329
Markdown (Informal)
[GAttention: Gated Attention for the Detection of Abusive Language](https://aclanthology.org/2025.findings-emnlp.1105/) (Vásquez et al., Findings 2025)
ACL