@inproceedings{kumaresan-etal-2026-findings,
title = "Findings of Shared Task on Counter Narrative Generation on Homophobic and Transphobic Comments",
author = "Kumaresan, Prasanna Kumar and
Prasannan, Praveen and
Singh, Tanay and
Priyadharshini, Ruba and
Navaneethakrishnan, Subalalitha Chinnaudayar and
Rajiakodi, Saranya and
Buitelaar, Paul and
Chakravarthi, Bharathi Raja",
editor = "Chakravarthi, Bharathi Raja and
B, Bharathi and
Buitelaar, Paul and
Thenmozhi, Durairaj and
Garc{\'i}a Cumbreras, Miguel {\'A}ngel and
Jim{\'e}nez Zafra, Salud Mar{\'i}a",
booktitle = "Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion",
month = jul,
year = "2026",
address = "Virtual (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.ltedi-1.11/",
pages = "118--127",
ISBN = "979-8-89176-424-8",
abstract = "Online platforms continue to witness harmful expressions targeting LGBTQ+ individuals, particularly in the form of homophobic and transphobic comments. While detection of such content has received substantial attention, generating constructive counter-narratives remains comparatively underexplored. In this shared task, we focus on counter-narrative generation in English and Tamil. Participants were provided with social media comments labeled as homophobic or transphobic and were required to generate respectful, contextually appropriate responses that challenge prejudice and promote empathy. Systems were evaluated using both reference-based metrics (Distinct-2 and BERTScore-F1) and rubric-based human evaluation metrics measuring politeness (PRS), quality (QS), and contextual coherence (CCNC). The results demonstrate variation in system performance across languages, with English systems showing stronger lexical diversity and Tamil systems excelling in politeness and contextual coherence. This paper presents dataset statistics, evaluation methodology, system performance analysis, and key observations from the shared task."
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<abstract>Online platforms continue to witness harmful expressions targeting LGBTQ+ individuals, particularly in the form of homophobic and transphobic comments. While detection of such content has received substantial attention, generating constructive counter-narratives remains comparatively underexplored. In this shared task, we focus on counter-narrative generation in English and Tamil. Participants were provided with social media comments labeled as homophobic or transphobic and were required to generate respectful, contextually appropriate responses that challenge prejudice and promote empathy. Systems were evaluated using both reference-based metrics (Distinct-2 and BERTScore-F1) and rubric-based human evaluation metrics measuring politeness (PRS), quality (QS), and contextual coherence (CCNC). The results demonstrate variation in system performance across languages, with English systems showing stronger lexical diversity and Tamil systems excelling in politeness and contextual coherence. This paper presents dataset statistics, evaluation methodology, system performance analysis, and key observations from the shared task.</abstract>
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%0 Conference Proceedings
%T Findings of Shared Task on Counter Narrative Generation on Homophobic and Transphobic Comments
%A Kumaresan, Prasanna Kumar
%A Prasannan, Praveen
%A Singh, Tanay
%A Priyadharshini, Ruba
%A Navaneethakrishnan, Subalalitha Chinnaudayar
%A Rajiakodi, Saranya
%A Buitelaar, Paul
%A Chakravarthi, Bharathi Raja
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Thenmozhi, Durairaj
%Y García Cumbreras, Miguel Ángel
%Y Jiménez Zafra, Salud María
%S Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2026
%8 July
%I Association for Computational Linguistics
%C Virtual (Online)
%@ 979-8-89176-424-8
%F kumaresan-etal-2026-findings
%X Online platforms continue to witness harmful expressions targeting LGBTQ+ individuals, particularly in the form of homophobic and transphobic comments. While detection of such content has received substantial attention, generating constructive counter-narratives remains comparatively underexplored. In this shared task, we focus on counter-narrative generation in English and Tamil. Participants were provided with social media comments labeled as homophobic or transphobic and were required to generate respectful, contextually appropriate responses that challenge prejudice and promote empathy. Systems were evaluated using both reference-based metrics (Distinct-2 and BERTScore-F1) and rubric-based human evaluation metrics measuring politeness (PRS), quality (QS), and contextual coherence (CCNC). The results demonstrate variation in system performance across languages, with English systems showing stronger lexical diversity and Tamil systems excelling in politeness and contextual coherence. This paper presents dataset statistics, evaluation methodology, system performance analysis, and key observations from the shared task.
%U https://aclanthology.org/2026.ltedi-1.11/
%P 118-127
Markdown (Informal)
[Findings of Shared Task on Counter Narrative Generation on Homophobic and Transphobic Comments](https://aclanthology.org/2026.ltedi-1.11/) (Kumaresan et al., LTEDI 2026)
ACL
- Prasanna Kumar Kumaresan, Praveen Prasannan, Tanay Singh, Ruba Priyadharshini, Subalalitha Chinnaudayar Navaneethakrishnan, Saranya Rajiakodi, Paul Buitelaar, and Bharathi Raja Chakravarthi. 2026. Findings of Shared Task on Counter Narrative Generation on Homophobic and Transphobic Comments. In Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 118–127, Virtual (Online). Association for Computational Linguistics.