Publications

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Conference Papers

Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework.

Published in Proceedings of the 2026 Annual Meeting of the Association for Computational Linguistics, 2026

Automated medical report generation for 3D PET/CT imaging is fundamentally challenged by the high-dimensional nature of volumetric data and a critical scarcity of annotated datasets, particularly for low-resource languages. This paper introduces VietPET-RoI, the first large-scale 3D PET/CT dataset with fine-grained RoI annotation for a low-resource language, and proposes HiRRA, a novel framework that mimics the professional radiologist diagnostic workflow.

Recommended citation: Nguyen, Cong Huy et al. “Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework.” (2026). ACL 2026.
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Global Beats, Local Tongue: Studying Code Switching in K-pop Hits on Billboard Charts

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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Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning

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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Revisiting the Classics: A Study on Identifying and Rectifying Gender Stereotypes in Rhymes and Poems

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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Preprints

Few-Shot Contrastive Adaptation for Audio Abuse Detection in Low-Resource Indic Languages

Published in , 2026

Abusive speech detection is becoming increasingly important as social media shifts towards voice-based interaction, particularly in multilingual and low-resource settings. This paper investigates whether Contrastive Language-Audio Pre-training (CLAP) can support abusive speech detection directly from audio, bypassing the vulnerabilities of ASR-based pipelines.

Recommended citation: Aditya Narayan Sankaran, Reza Farahbakhsh, and Noel Crespi. 2025. Few-Shot Contrastive Adaptation for Audio Abuse Detection in Low-Resource Indic Languages. Preprint.
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Aditya Narayan SANKARAN