2024
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Overview of Shared Task on Multitask Meme Classification - Unraveling Misogynistic and Trolls in Online Memes
Bharathi Raja Chakravarthi
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Saranya Rajiakodi
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Rahul Ponnusamy
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Kathiravan Pannerselvam
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Anand Kumar Madasamy
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Ramachandran Rajalakshmi
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Hariharan LekshmiAmmal
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Anshid Kizhakkeparambil
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Susminu S Kumar
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Bhuvaneswari Sivagnanam
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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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Overview of Shared Task on Caste and Migration Hate Speech Detection
Saranya Rajiakodi
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Bharathi Raja Chakravarthi
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Rahul Ponnusamy
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Prasanna Kumaresan
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Sathiyaraj Thangasamy
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Bhuvaneswari Sivagnanam
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Charmathi Rajkumar
Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
We present an overview of the first shared task on “Caste and Migration Hate Speech Detection.” The shared task is organized as part of LTEDI@EACL 2024. The system must delineate between binary outcomes, ascertaining whether the text is categorized as a caste/migration hate speech or not. The dataset presented in this shared task is in Tamil, which is one of the under-resource languages. There are a total of 51 teams participated in this task. Among them, 15 teams submitted their research results for the task. To the best of our knowledge, this is the first time the shared task has been conducted on textual hate speech detection concerning caste and migration. In this study, we have conducted a systematic analysis and detailed presentation of all the contributions of the participants as well as the statistics of the dataset, which is the social media comments in Tamil language to detect hate speech. It also further goes into the details of a comprehensive analysis of the participants’ methodology and their findings.
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Overview of Second Shared Task on Sentiment Analysis in Code-mixed Tamil and Tulu
Lavanya Sambath Kumar
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Asha Hegde
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Bharathi Raja Chakravarthi
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Hosahalli Shashirekha
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Rajeswari Natarajan
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Sajeetha Thavareesan
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Ratnasingam Sakuntharaj
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Thenmozhi Durairaj
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Prasanna Kumar Kumaresan
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Charmathi Rajkumar
Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
Sentiment Analysis (SA) in Dravidian codemixed text is a hot research area right now. In this regard, the “Second Shared Task on SA in Code-mixed Tamil and Tulu” at Dravidian- LangTech (EACL-2024) is organized. Two tasks namely SA in Tamil-English and Tulu- English code-mixed data, make up this shared assignment. In total, 64 teams registered for the shared task, out of which 19 and 17 systems were received for Tamil and Tulu, respectively. The performance of the systems submitted by the participants was evaluated based on the macro F1-score. The best method obtained macro F1-scores of 0.260 and 0.584 for code-mixed Tamil and Tulu texts, respectively.
2023
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VEL@DravidianLangTech: Sentiment Analysis of Tamil and Tulu
Kishore Kumar Ponnusamy
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Charmathi Rajkumar
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Prasanna Kumar Kumaresan
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Elizabeth Sherly
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Ruba Priyadharshini
Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages
We participated in the Sentiment Analysis in Tamil and Tulu - DravidianLangTech 2023-RANLP 2023 task in the team name of VEL. This research focuses on addressing the challenge of detecting sentiment analysis in social media code-mixed comments written in Tamil and Tulu languages. Code-mixed text in social media often deviates from strict grammar rules and incorporates non-native scripts, making sentiment identification a complex task. To tackle this issue, we employ pre-processing techniques to remove unnecessary content and develop a model specifically designed for sentiment analysis detection. Additionally, we explore the effectiveness of traditional machine-learning models combined with feature extraction techniques. Our best model logistic regression configurations achieve impressive macro F1 scores of 0.43 on the Tamil test set and 0.51 on the Tulu test set, indicating promising results in accurately detecting instances of sentiment in code-mixed comments.