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    <abstract>Homophobic/Transphobic (H/T) content includes hate speech, discrimination text, and abusive comments against Gay, Lesbian, Bisexual, Transgender, Queer, and Intersex (LGBTQ) individuals. With the increase in user generated text in social media, there has been an increase in code-mixed H/T content, which poses challenges for efficient analysis and detection of H/T content on social media. The complex nature of code-mixed text necessitates the development of advanced tools and techniques to effectively tackle this issue in social media platforms. To tackle this issue, in this paper, we - team MUCS, describe the transformer based models submitted to “Homophobia/Transphobia Detection in social media comments” shared task in Language Technology for Equality, Diversity and Inclusion (LT-EDI) at Recent Advances in Natural Language Processing (RANLP)-2023. The proposed methodology makes use of resampling the training data to handle the data imbalance and this resampled data is used to fine-tune the Multilingual Bidirectional Encoder Representations from Transformers (mBERT) models. These models obtained 11th, 5th, 3rd, 3rd, and 7th ranks for English, Tamil, Malayalam, Spanish, and Hindi respectively in Task A and 8th, 2nd, and 2nd ranks for English, Tamil, and Malayalam respectively in Task B.</abstract>
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%0 Conference Proceedings
%T MUCS@LT-EDI2023: Homophobic/Transphobic Content Detection in Social Media Text using mBERT
%A Hegde, Asha
%A G, Kavya
%A Coelho, Sharal
%A Shashirekha, Hosahalli Lakshmaiah
%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 hegde-etal-2023-mucs-lt-edi2023
%X Homophobic/Transphobic (H/T) content includes hate speech, discrimination text, and abusive comments against Gay, Lesbian, Bisexual, Transgender, Queer, and Intersex (LGBTQ) individuals. With the increase in user generated text in social media, there has been an increase in code-mixed H/T content, which poses challenges for efficient analysis and detection of H/T content on social media. The complex nature of code-mixed text necessitates the development of advanced tools and techniques to effectively tackle this issue in social media platforms. To tackle this issue, in this paper, we - team MUCS, describe the transformer based models submitted to “Homophobia/Transphobia Detection in social media comments” shared task in Language Technology for Equality, Diversity and Inclusion (LT-EDI) at Recent Advances in Natural Language Processing (RANLP)-2023. The proposed methodology makes use of resampling the training data to handle the data imbalance and this resampled data is used to fine-tune the Multilingual Bidirectional Encoder Representations from Transformers (mBERT) models. These models obtained 11th, 5th, 3rd, 3rd, and 7th ranks for English, Tamil, Malayalam, Spanish, and Hindi respectively in Task A and 8th, 2nd, and 2nd ranks for English, Tamil, and Malayalam respectively in Task B.
%U https://aclanthology.org/2023.ltedi-1.44
%P 287-294

Markdown (Informal)

[MUCS@LT-EDI2023: Homophobic/Transphobic Content Detection in Social Media Text using mBERT](https://aclanthology.org/2023.ltedi-1.44) (Hegde et al., LTEDI-WS 2023)

• MUCS@LT-EDI2023: Homophobic/Transphobic Content Detection in Social Media Text using mBERT (Hegde et al., LTEDI-WS 2023)

ACL

• Asha Hegde, Kavya G, Sharal Coelho, and Hosahalli Lakshmaiah Shashirekha. 2023. MUCS@LT-EDI2023: Homophobic/Transphobic Content Detection in Social Media Text using mBERT. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 287–294, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.