Hariharan LekshmiAmmal


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Overview of Shared Task on Multitask Meme Classification - Unraveling Misogynistic and Trolls in Online Memes
Bharathi Raja Chakravarthi | Saranya Rajiakodi | Rahul Ponnusamy | Kathiravan Pannerselvam | Anand Kumar Madasamy | Ramachandran Rajalakshmi | Hariharan LekshmiAmmal | Anshid Kizhakkeparambil | Susminu S Kumar | Bhuvaneswari Sivagnanam | Charmathi Rajkumar
Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion

This paper offers a detailed overview of the first shared task on “Multitask Meme Classification - Unraveling Misogynistic and Trolls in Online Memes,” organized as part of the LT-EDI@EACL 2024 conference. The task was set to classify misogynistic content and troll memes within online platforms, focusing specifically on memes in Tamil and Malayalam languages. A total of 52 teams registered for the competition, with four submitting systems for the Tamil meme classification task and three for the Malayalam task. The outcomes of this shared task are significant, providing insights into the current state of misogynistic content in digital memes and highlighting the effectiveness of various computational approaches in identifying such detrimental content. The top-performing model got a macro F1 score of 0.73 in Tamil and 0.87 in Malayalam.


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NITK-IT_NLP@TamilNLP-ACL2022: Transformer based model for Toxic Span Identification in Tamil
Hariharan LekshmiAmmal | Manikandan Ravikiran | Anand Kumar Madasamy
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages

Toxic span identification in Tamil is a shared task that focuses on identifying harmful content, contributing to offensiveness. In this work, we have built a model that can efficiently identify the span of text contributing to offensive content. We have used various transformer-based models to develop the system, out of which the fine-tuned MuRIL model was able to achieve the best overall character F1-score of 0.4489.