D eep B lue AI @ D ravidian L ang T ech- RANLP 2023 (2023)

Abstract: This paper presents a study on the language understanding of the Dravidian languages. Three specific tasks related to text classification are focused on in this study, including abusive comment detection, sentiment analysis and fake news detection. The paper provides a detailed description of the tasks, including dataset information and task definitions, as well as the model architectures and training details used to tackle them. Finally, the competition results are presented, demonstrating the effectiveness of the proposed approach for handling these challenging NLP tasks in the context of the Dravidian languages.

## introduction the field of natural language processing (nlp) has made significant progress in recent years, with the development of increasingly powerful models and techniques for understanding and analyzing human language. however, one major challenge that remains is the development of nlp systems that can effectively handle regional and under-resourced languages @xcite , which often lack the resources and support needed for effective nlp research. the dravidian languages @xcite are one such family of languages that have received relatively little attention in the nlp community, despite their significant cultural and linguistic importance. to address this gap, this paper focuses on the language understanding of the dravidian languages, with a particular emphasis on five specific tasks related to text classification. these tasks include abusive comment detection, sentiment analysis and fake news detection. as the transformer model @xcite ) gained popularity, various pretrained models have been proposed, including bert @xcite , roberta @xcite , and the cross lingual pretrained model xlm-roberta (conneau et al., 2020), etc. meanwhile, finetuning of pretrained language models has gradually become the standard approach of various natural language understanding tasks, including text classification, as demonstrated in this paper. in this paper, we provide a detailed description of these tasks, including the dataset information and task definitions, as well as the model architectures and training details used to tackle them. we also present our competition results, demonstrating the effectiveness of our approach for handling these challenging nlp tasks in the context of the dravidian languages. ## task description in this section, we describe the task definition and dataset of the 3 tasks we participated in. ## experiments this section provides a detailed account of the training settings used in our experiments. ## competition results our team participated in this competition and achieved promising results, earning 4 first place rankings as well as 1 second place, as shown in