Shankar Mahadevan


Thirumurai: A Large Dataset of Tamil Shaivite Poems and Classification of Tamil Pann
Shankar Mahadevan | Rahul Ponnusamy | Prasanna Kumar Kumaresan | Prabakaran Chandran | Ruba Priyadharshini | Sangeetha S | Bharathi Raja Chakravarthi
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Thirumurai, also known as Panniru Thirumurai, is a collection of Tamil Shaivite poems dating back to the Hindu revival period between the 6th and the 10th century. These poems are par excellence, in both literary and musical terms. They have been composed based on the ancient, now non-existent Tamil Pann system and can be set to music. We present a large dataset containing all the Thirumurai poems and also attempt to classify the Pann and author of each poem using transformer based architectures. Our work is the first of its kind in dealing with ancient Tamil text datasets, which are severely under-resourced. We explore several Deep Learning-based techniques for solving this challenge effectively and provide essential insights into the problem and how to address it.

Transformers at SemEval-2022 Task 5: A Feature Extraction based Approach for Misogynous Meme Detection
Shankar Mahadevan | Sean Benhur | Roshan Nayak | Malliga Subramanian | Kogilavani Shanmugavadivel | Kanchana Sivanraju | Bharathi Raja Chakravarthi
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Social media is an idea created to make theworld smaller and more connected. Recently,it has become a hub of fake news and sexistmemes that target women. Social Media shouldensure proper women’s safety and equality. Filteringsuch information from social media is ofparamount importance to achieving this goal.In this paper, we describe the system developedby our team for SemEval-2022 Task 5: MultimediaAutomatic Misogyny Identification. Wepropose a multimodal training methodologythat achieves good performance on both thesubtasks, ranking 4th for Subtask A (0.718macro F1-score) and 9th for Subtask B (0.695macro F1-score) while exceeding the baselineresults by good margins.

Findings of the Shared Task on Offensive Span Identification fromCode-Mixed Tamil-English Comments
Manikandan Ravikiran | Bharathi Raja Chakravarthi | Anand Kumar Madasamy | Sangeetha S | Ratnavel Rajalakshmi | Sajeetha Thavareesan | Rahul Ponnusamy | Shankar Mahadevan
Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages

Offensive content moderation is vital in social media platforms to support healthy online discussions. However, their prevalence in code-mixed Dravidian languages is limited to classifying whole comments without identifying part of it contributing to offensiveness. Such limitation is primarily due to the lack of annotated data for offensive spans. Accordingly, in this shared task, we provide Tamil-English code-mixed social comments with offensive spans. This paper outlines the dataset so released, methods, and results of the submitted systems.