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            <namePart type="given">Mohit</namePart>
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    <abstract>Hate speech (HS) is a widely acknowledged societal problem with potentially grave effects on vulnerable individuals and minority groups. Developing counter-narratives (CNs) that confront biases and stereotypes driving hateful narratives is considered an impactful strategy. Current automatic methods focus on isolated utterances to detect and react to hateful content online, often omitting the conversational context where HS naturally occurs. In this work, we explore strategies for the incorporation of conversational history for CN generation, comparing text and graphical representations with varying degrees of context. Overall, automatic and human evaluations show that 1) contextualized representations are comparable to those of isolated utterances, and 2) models based on graph representations outperform text representations, thus opening new research directions for future work.</abstract>
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%0 Conference Proceedings
%T Contextualized Graph Representations for Generating Counter-Narratives against Hate Speech
%A Baez Santamaria, Selene
%A Gomez Adorno, Helena
%A Markov, Ilia
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F baez-santamaria-etal-2024-contextualized
%X Hate speech (HS) is a widely acknowledged societal problem with potentially grave effects on vulnerable individuals and minority groups. Developing counter-narratives (CNs) that confront biases and stereotypes driving hateful narratives is considered an impactful strategy. Current automatic methods focus on isolated utterances to detect and react to hateful content online, often omitting the conversational context where HS naturally occurs. In this work, we explore strategies for the incorporation of conversational history for CN generation, comparing text and graphical representations with varying degrees of context. Overall, automatic and human evaluations show that 1) contextualized representations are comparable to those of isolated utterances, and 2) models based on graph representations outperform text representations, thus opening new research directions for future work.
%U https://aclanthology.org/2024.findings-emnlp.450
%P 7664-7674

Markdown (Informal)

[Contextualized Graph Representations for Generating Counter-Narratives against Hate Speech](https://aclanthology.org/2024.findings-emnlp.450) (Baez Santamaria et al., Findings 2024)

• Contextualized Graph Representations for Generating Counter-Narratives against Hate Speech (Baez Santamaria et al., Findings 2024)

ACL

• Selene Baez Santamaria, Helena Gomez Adorno, and Ilia Markov. 2024. Contextualized Graph Representations for Generating Counter-Narratives against Hate Speech. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 7664–7674, Miami, Florida, USA. Association for Computational Linguistics.