Abstract
Machine learning and deep learning models have shown great potential in detecting hate speech from social media posts. This study focuses on the homophobia and transphobia detection task of LT-EDI-2024 in English. Several machine learning models, a Deep Neural Network (DNN), and the Bidirectional Encoder Representations from Transformers (BERT) model have been trained on the provided dataset using different feature vectorization techniques. We secured top rank with the best macro-F1 score of 0.4963, which was achieved by fine-tuning the BERT model on the English test set.- Anthology ID:
- 2024.ltedi-1.36
- Volume:
- Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian's, Malta
- Editors:
- Bharathi Raja Chakravarthi, Bharathi B, Paul Buitelaar, Thenmozhi Durairaj, György Kovács, Miguel Ángel García Cumbreras
- Venues:
- LTEDI | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 271–276
- Language:
- URL:
- https://aclanthology.org/2024.ltedi-1.36
- DOI:
- Cite (ACL):
- Sargam Yadav, Abhishek Kaushik, and Kevin McDaid. 2024. dkit@LT-EDI-2024: Detecting Homophobia and Transphobia in English Social Media Comments. In Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 271–276, St. Julian's, Malta. Association for Computational Linguistics.
- Cite (Informal):
- dkit@LT-EDI-2024: Detecting Homophobia and Transphobia in English Social Media Comments (Yadav et al., LTEDI-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.ltedi-1.36.pdf