Nafisa Tabassum


2024

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Sandalphon@DravidianLangTech-EACL2024: Hate and Offensive Language Detection in Telugu Code-mixed Text using Transliteration-Augmentation
Nafisa Tabassum | Mosabbir Khan | Shawly Ahsan | Jawad Hossain | Mohammed Moshiul Hoque
Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages

Hate and offensive language in online platforms pose significant challenges, necessitating automatic detection methods. Particularly in the case of codemixed text, which is very common in social media, the complexity of this problem increases due to the cultural nuances of different languages. DravidianLangTech-EACL2024 organized a shared task on detecting hate and offensive language for Telugu. To complete this task, this study investigates the effectiveness of transliteration-augmented datasets for Telugu code-mixed text. In this work, we compare the performance of various machine learning (ML), deep learning (DL), and transformer-based models on both original and augmented datasets. Experimental findings demonstrate the superiority of transformer models, particularly Telugu-BERT, achieving the highest f1-score of 0.77 on the augmented dataset, ranking the 1st position in the leaderboard. The study highlights the potential of transliteration-augmented datasets in improving model performance and suggests further exploration of diverse transliteration options to address real-world scenarios.

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Punny_Punctuators@DravidianLangTech-EACL2024: Transformer-based Approach for Detection and Classification of Fake News in Malayalam Social Media Text
Nafisa Tabassum | Sumaiya Aodhora | Rowshon Akter | Jawad Hossain | Shawly Ahsan | Mohammed Moshiul Hoque
Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages

The alarming rise of fake news on social media poses a significant threat to public discourse and decision-making. While automatic detection of fake news offers a promising solution, research in low-resource languages like Malayalam often falls behind due to limited data and tools. This paper presents the participation of team Punny_Punctuators in the Fake News Detection in Dravidian Languages shared task at DravidianLangTech@EACL 2024, addressing this gap. The shared task focuses on two sub-tasks: 1. classifying social media texts as original or fake, and 2. categorizing fake news into 5 categories. We experimented with various machine learning (ML), deep learning (DL) and transformer-based models as well as processing techniques such as transliteration. Malayalam-BERT achieved the best performance on both sub-tasks, which obtained us 2nd place with a macro f1-score of 0.87 for the subtask-1 and 11th place with a macro f1-score of 0.17 for the subtask-2. Our results highlight the potential of transformer models for low-resource languages in fake news detection and pave the way for further research in this crucial area.