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Published in 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), 2024
This study tries to fill the gap by using various machine-learning techniques to reduce the amount of human intervention to rectify such stereotypes.
Recommended citation: Aditya Narayan Sankaran, Vigneshwaran Shankaran, Sampath Lonka, and Rajesh Sharma. 2024. Revisiting the Classics: A Study on Identifying and Rectifying Gender Stereotypes in Rhymes and Poems. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 14092–14102, Torino, Italia. ELRA and ICCL.
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Published in Proceedings of the 31st International Conference on Computational Linguistics (COLING 2025), 2025
This paper presents a novel approach to cross-lingual audio abuse detection in low-resource settings using few-shot learning techniques.
Recommended citation: Aditya Narayan Sankaran, Reza Farahbakhsh, and Noel Crespi. 2025. Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning. In Proceedings of the 31st International Conference on Computational Linguistics, pages 5558–5569, Abu Dhabi, UAE. Association for Computational Linguistics.
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Published in Anthology of Computers and the Humanities, 2025
This paper is a primary investigation into the linguistic strategies employed in K-pop songs that achieve global chart success, with a focus on the role of code-switching and English lyric usage.
Recommended citation: Aditya Narayan Sankaran, Reza Farahbakhsh, and Noel Crespi. 2025. Global Beats, Local Tongue: Studying Code Switching in K-pop Hits on Billboard Charts. In Proceedings of the Anthology of Computers and the Humanities.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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