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    <abstract>Hate speech remains prevalent in human society and continues to evolve in its forms and expressions. Modern advancements in the internet and online anonymity accelerate its rapid spread and complicate its detection. However, hate speech datasets exhibit diverse characteristics primarily because they are constructed from different sources and platforms, each reflecting different linguistic styles and social contexts. Despite this diversity, prior studies on hate speech detection often rely on fixed methodologies without adapting to data-specific features. We introduce RV-HATE, a detection framework designed to account for the dataset-specific characteristics of each hate speech dataset. RV-HATE consists of multiple specialized modules, where each module focuses on distinct linguistic or contextual features of hate speech. The framework employs reinforcement learning to optimize weights that determine the contribution of each module for a given dataset. A voting mechanism then aggregates the module outputs to produce the final decision. RV-HATE offers two primary advantages: (1) it improves detection accuracy by tailoring the detection process to dataset-specific attributes, and (2) it also provides interpretable insights into the distinctive features of each dataset. Consequently, our approach effectively addresses implicit hate speech and achieves superior performance compared to conventional static methods.</abstract>
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%0 Conference Proceedings
%T RV-HATE: Reinforced Multi-Module Voting for Implicit Hate Speech Detection
%A Lee, Yejin
%A An, Hyeseon
%A Han, Yo-Sub
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F lee-etal-2026-rv
%X Hate speech remains prevalent in human society and continues to evolve in its forms and expressions. Modern advancements in the internet and online anonymity accelerate its rapid spread and complicate its detection. However, hate speech datasets exhibit diverse characteristics primarily because they are constructed from different sources and platforms, each reflecting different linguistic styles and social contexts. Despite this diversity, prior studies on hate speech detection often rely on fixed methodologies without adapting to data-specific features. We introduce RV-HATE, a detection framework designed to account for the dataset-specific characteristics of each hate speech dataset. RV-HATE consists of multiple specialized modules, where each module focuses on distinct linguistic or contextual features of hate speech. The framework employs reinforcement learning to optimize weights that determine the contribution of each module for a given dataset. A voting mechanism then aggregates the module outputs to produce the final decision. RV-HATE offers two primary advantages: (1) it improves detection accuracy by tailoring the detection process to dataset-specific attributes, and (2) it also provides interpretable insights into the distinctive features of each dataset. Consequently, our approach effectively addresses implicit hate speech and achieves superior performance compared to conventional static methods.
%R 10.18653/v1/2026.acl-long.2104
%U https://aclanthology.org/2026.acl-long.2104/
%U https://doi.org/10.18653/v1/2026.acl-long.2104
%P 45364-45383

Markdown (Informal)

[RV-HATE: Reinforced Multi-Module Voting for Implicit Hate Speech Detection](https://aclanthology.org/2026.acl-long.2104/) (Lee et al., ACL 2026)

• RV-HATE: Reinforced Multi-Module Voting for Implicit Hate Speech Detection (Lee et al., ACL 2026)

ACL

• Yejin Lee, Hyeseon An, and Yo-Sub Han. 2026. RV-HATE: Reinforced Multi-Module Voting for Implicit Hate Speech Detection. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 45364–45383, San Diego, California, United States. Association for Computational Linguistics.