@inproceedings{kulal-etal-2024-mucs,
title = "{MUCS}@{LT}-{EDI}-2024: Learning Approaches to Empower Homophobic/Transphobic Comment Identification",
author = "Kulal, Sonali and
Gidnakanala, Nethravathi and
G, Raksha and
G, Kavya and
Hegde, Asha and
Shashirekha, H",
editor = {Chakravarthi, Bharathi Raja and
B, Bharathi and
Buitelaar, Paul and
Durairaj, Thenmozhi and
Kov{\'a}cs, Gy{\"o}rgy and
Garc{\'\i}a Cumbreras, Miguel {\'A}ngel},
booktitle = "Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.ltedi-1.39",
pages = "288--293",
abstract = "Homophobic/Transphobic (H/T) content includes hatred and discriminatory comments directed at Lesbian, Gay, Bisexual, Transgender, Queer (LGBTQ) individuals on social media platforms. As this unfavourable perception towards LGBTQ individuals may affect them physically and mentally, it is necessary to detect H/T content on social media. This demands automated tools to identify and address H/T content. In view of this, in this paper, we - team MUCS describe the learning models submitted to {``}Homophobia/Transphobia Detection in social media comments:LT-EDI@EACL 2024{''} shared task at European Chapter of the Association for Computational Linguistics (EACL) 2024. The learning models: i) Homo{\_}Ensemble - an ensemble of Machine Learning (ML) algorithms trained with Term Frequency-Inverse Document Frequency (TFIDF) of syllable n-grams in the range (1, 3), ii) Homo{\_}TL - a model based on Transfer Learning (TL) approach with Bidirectional Encoder Representations from Transformers (BERT) models, iii) Homo{\_}probfuse - an ensemble of ML classifiers with soft voting trained using sentence embeddings (except for Hindi), and iv) Homo{\_}FSL - Few-Shot Learning (FSL) models using Sentence Transformer (ST) (only for Tulu), are proposed to detect H/T content in the given languages. Among the models submitted to the shared task, the models that performed better for each language include: i) Homo{\_}Ensemble model obtained macro F1 score of 0.95 securing 4th rank for Telugu language, ii) Homo{\_}TL model obtained macro F1 scores of 0.49, 0.53, 0.45, 0.94, and 0.95 securing 2nd, 2nd, 1st, 1st, and 4th ranks for English, Marathi, Hindi, Kannada, and Gujarathi languages, respectively, iii) Homo{\_}probfuse model obtained macro F1 scores of 0.86, 0.87, and 0.53 securing 2nd, 6th, and 2nd ranks for Tamil, Malayalam, and Spanish languages respectively, and iv) Homo{\_}FSL model obtained a macro F1 score of 0.62 securing 2nd rank for Tulu dataset.",
}
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<abstract>Homophobic/Transphobic (H/T) content includes hatred and discriminatory comments directed at Lesbian, Gay, Bisexual, Transgender, Queer (LGBTQ) individuals on social media platforms. As this unfavourable perception towards LGBTQ individuals may affect them physically and mentally, it is necessary to detect H/T content on social media. This demands automated tools to identify and address H/T content. In view of this, in this paper, we - team MUCS describe the learning models submitted to “Homophobia/Transphobia Detection in social media comments:LT-EDI@EACL 2024” shared task at European Chapter of the Association for Computational Linguistics (EACL) 2024. The learning models: i) Homo_Ensemble - an ensemble of Machine Learning (ML) algorithms trained with Term Frequency-Inverse Document Frequency (TFIDF) of syllable n-grams in the range (1, 3), ii) Homo_TL - a model based on Transfer Learning (TL) approach with Bidirectional Encoder Representations from Transformers (BERT) models, iii) Homo_probfuse - an ensemble of ML classifiers with soft voting trained using sentence embeddings (except for Hindi), and iv) Homo_FSL - Few-Shot Learning (FSL) models using Sentence Transformer (ST) (only for Tulu), are proposed to detect H/T content in the given languages. Among the models submitted to the shared task, the models that performed better for each language include: i) Homo_Ensemble model obtained macro F1 score of 0.95 securing 4th rank for Telugu language, ii) Homo_TL model obtained macro F1 scores of 0.49, 0.53, 0.45, 0.94, and 0.95 securing 2nd, 2nd, 1st, 1st, and 4th ranks for English, Marathi, Hindi, Kannada, and Gujarathi languages, respectively, iii) Homo_probfuse model obtained macro F1 scores of 0.86, 0.87, and 0.53 securing 2nd, 6th, and 2nd ranks for Tamil, Malayalam, and Spanish languages respectively, and iv) Homo_FSL model obtained a macro F1 score of 0.62 securing 2nd rank for Tulu dataset.</abstract>
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%0 Conference Proceedings
%T MUCS@LT-EDI-2024: Learning Approaches to Empower Homophobic/Transphobic Comment Identification
%A Kulal, Sonali
%A Gidnakanala, Nethravathi
%A G, Raksha
%A G, Kavya
%A Hegde, Asha
%A Shashirekha, H.
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Durairaj, Thenmozhi
%Y Kovács, György
%Y García Cumbreras, Miguel Ángel
%S Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F kulal-etal-2024-mucs
%X Homophobic/Transphobic (H/T) content includes hatred and discriminatory comments directed at Lesbian, Gay, Bisexual, Transgender, Queer (LGBTQ) individuals on social media platforms. As this unfavourable perception towards LGBTQ individuals may affect them physically and mentally, it is necessary to detect H/T content on social media. This demands automated tools to identify and address H/T content. In view of this, in this paper, we - team MUCS describe the learning models submitted to “Homophobia/Transphobia Detection in social media comments:LT-EDI@EACL 2024” shared task at European Chapter of the Association for Computational Linguistics (EACL) 2024. The learning models: i) Homo_Ensemble - an ensemble of Machine Learning (ML) algorithms trained with Term Frequency-Inverse Document Frequency (TFIDF) of syllable n-grams in the range (1, 3), ii) Homo_TL - a model based on Transfer Learning (TL) approach with Bidirectional Encoder Representations from Transformers (BERT) models, iii) Homo_probfuse - an ensemble of ML classifiers with soft voting trained using sentence embeddings (except for Hindi), and iv) Homo_FSL - Few-Shot Learning (FSL) models using Sentence Transformer (ST) (only for Tulu), are proposed to detect H/T content in the given languages. Among the models submitted to the shared task, the models that performed better for each language include: i) Homo_Ensemble model obtained macro F1 score of 0.95 securing 4th rank for Telugu language, ii) Homo_TL model obtained macro F1 scores of 0.49, 0.53, 0.45, 0.94, and 0.95 securing 2nd, 2nd, 1st, 1st, and 4th ranks for English, Marathi, Hindi, Kannada, and Gujarathi languages, respectively, iii) Homo_probfuse model obtained macro F1 scores of 0.86, 0.87, and 0.53 securing 2nd, 6th, and 2nd ranks for Tamil, Malayalam, and Spanish languages respectively, and iv) Homo_FSL model obtained a macro F1 score of 0.62 securing 2nd rank for Tulu dataset.
%U https://aclanthology.org/2024.ltedi-1.39
%P 288-293
Markdown (Informal)
[MUCS@LT-EDI-2024: Learning Approaches to Empower Homophobic/Transphobic Comment Identification](https://aclanthology.org/2024.ltedi-1.39) (Kulal et al., LTEDI-WS 2024)
ACL
- Sonali Kulal, Nethravathi Gidnakanala, Raksha G, Kavya G, Asha Hegde, and H Shashirekha. 2024. MUCS@LT-EDI-2024: Learning Approaches to Empower Homophobic/Transphobic Comment Identification. In Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 288–293, St. Julian's, Malta. Association for Computational Linguistics.