@inproceedings{kumaresan-etal-2023-vel,
title = "{VEL}@{LT}-{EDI}: Detecting Homophobia and Transphobia in Code-Mixed {S}panish Social Media Comments",
author = "Kumaresan, Prasanna Kumar and
Ponnusamy, Kishore Kumar and
S V, Kogilavani and
Cn, Subalalitha and
Priyadharshini, Ruba and
Chakravarthi, Bharathi Raja",
editor = "Chakravarthi, Bharathi R. and
Bharathi, B. and
Griffith, Joephine and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ltedi-1.35",
pages = "233--238",
abstract = "Our research aims to address the task of detecting homophobia and transphobia in social media code-mixed comments written in Spanish. Code-mixed text in social media often violates strict grammar rules and incorporates non-native scripts, posing challenges for identification. To tackle this problem, we perform pre-processing by removing unnecessary content and establishing a baseline for detecting homophobia and transphobia. Furthermore, we explore the effectiveness of various traditional machine-learning models with feature extraction and pre-trained transformer model techniques. Our best configurations achieve macro F1 scores of 0.84 on the test set and 0.82 on the development set for Spanish, demonstrating promising results in detecting instances of homophobia and transphobia in code-mixed comments.",
}
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<abstract>Our research aims to address the task of detecting homophobia and transphobia in social media code-mixed comments written in Spanish. Code-mixed text in social media often violates strict grammar rules and incorporates non-native scripts, posing challenges for identification. To tackle this problem, we perform pre-processing by removing unnecessary content and establishing a baseline for detecting homophobia and transphobia. Furthermore, we explore the effectiveness of various traditional machine-learning models with feature extraction and pre-trained transformer model techniques. Our best configurations achieve macro F1 scores of 0.84 on the test set and 0.82 on the development set for Spanish, demonstrating promising results in detecting instances of homophobia and transphobia in code-mixed comments.</abstract>
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%0 Conference Proceedings
%T VEL@LT-EDI: Detecting Homophobia and Transphobia in Code-Mixed Spanish Social Media Comments
%A Kumaresan, Prasanna Kumar
%A Ponnusamy, Kishore Kumar
%A S V, Kogilavani
%A Cn, Subalalitha
%A Priyadharshini, Ruba
%A Chakravarthi, Bharathi Raja
%Y Chakravarthi, Bharathi R.
%Y Bharathi, B.
%Y Griffith, Joephine
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F kumaresan-etal-2023-vel
%X Our research aims to address the task of detecting homophobia and transphobia in social media code-mixed comments written in Spanish. Code-mixed text in social media often violates strict grammar rules and incorporates non-native scripts, posing challenges for identification. To tackle this problem, we perform pre-processing by removing unnecessary content and establishing a baseline for detecting homophobia and transphobia. Furthermore, we explore the effectiveness of various traditional machine-learning models with feature extraction and pre-trained transformer model techniques. Our best configurations achieve macro F1 scores of 0.84 on the test set and 0.82 on the development set for Spanish, demonstrating promising results in detecting instances of homophobia and transphobia in code-mixed comments.
%U https://aclanthology.org/2023.ltedi-1.35
%P 233-238
Markdown (Informal)
[VEL@LT-EDI: Detecting Homophobia and Transphobia in Code-Mixed Spanish Social Media Comments](https://aclanthology.org/2023.ltedi-1.35) (Kumaresan et al., LTEDI-WS 2023)
ACL
- Prasanna Kumar Kumaresan, Kishore Kumar Ponnusamy, Kogilavani S V, Subalalitha Cn, Ruba Priyadharshini, and Bharathi Raja Chakravarthi. 2023. VEL@LT-EDI: Detecting Homophobia and Transphobia in Code-Mixed Spanish Social Media Comments. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 233–238, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.