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        <title>Identifying Aggression and Offensive Language in Code-Mixed Tweets: A Multi-Task Transfer Learning Approach</title>
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    <abstract>The widespread use of social media has contributed to the increase in hate speech and offensive language, impacting people of all ages. This issue is particularly difficult to address when the text is in a code-mixed language. Twitter is commonly used to express opinions in code-mixed language. In this paper, we introduce a novel Multi-Task Transfer Learning (MTTL) framework to detect aggression and offensive language. By focusing on the dual facets of cyberbullying, aggressiveness and offensiveness, our model leverages the MTTL approach to enhance the performance of the model on the aggression and offensive language detection. Results show that our Multi-Task Transfer Learning (MTTL) setup significantly enhances the performance of state-of-the-art pretrained language models, BERT, RoBERTa, and Hing-RoBERTa for Hindi-English code-mixed data from Twitter.</abstract>
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%0 Conference Proceedings
%T Identifying Aggression and Offensive Language in Code-Mixed Tweets: A Multi-Task Transfer Learning Approach
%A Kancharla, Bharath
%A Singh, Prabhjot
%A Kancharla, Lohith Bhagavan
%A Chama, Yashita
%A Sharma, Raksha
%Y Weerasinghe, Ruvan
%Y Anuradha, Isuri
%Y Sumanathilaka, Deshan
%S Proceedings of the First Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi
%F kancharla-etal-2025-identifying
%X The widespread use of social media has contributed to the increase in hate speech and offensive language, impacting people of all ages. This issue is particularly difficult to address when the text is in a code-mixed language. Twitter is commonly used to express opinions in code-mixed language. In this paper, we introduce a novel Multi-Task Transfer Learning (MTTL) framework to detect aggression and offensive language. By focusing on the dual facets of cyberbullying, aggressiveness and offensiveness, our model leverages the MTTL approach to enhance the performance of the model on the aggression and offensive language detection. Results show that our Multi-Task Transfer Learning (MTTL) setup significantly enhances the performance of state-of-the-art pretrained language models, BERT, RoBERTa, and Hing-RoBERTa for Hindi-English code-mixed data from Twitter.
%U https://aclanthology.org/2025.indonlp-1.14/
%P 122-128

Markdown (Informal)

[Identifying Aggression and Offensive Language in Code-Mixed Tweets: A Multi-Task Transfer Learning Approach](https://aclanthology.org/2025.indonlp-1.14/) (Kancharla et al., IndoNLP 2025)

• Identifying Aggression and Offensive Language in Code-Mixed Tweets: A Multi-Task Transfer Learning Approach (Kancharla et al., IndoNLP 2025)

ACL

• Bharath Kancharla, Prabhjot Singh, Lohith Bhagavan Kancharla, Yashita Chama, and Raksha Sharma. 2025. Identifying Aggression and Offensive Language in Code-Mixed Tweets: A Multi-Task Transfer Learning Approach. In Proceedings of the First Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, pages 122–128, Abu Dhabi. Association for Computational Linguistics.