This paper presents a novel approach to hate speech detection and target identification across Devanagari-script languages, with a focus on Hindi and Nepali. Leveraging an Attention BiLSTM-XLM-RoBERTa architecture, our model effectively captures language-specific features and sequential dependencies crucial for multilingual natural language understanding (NLU). In Task B (Hate Speech Detection), our model achieved a Macro F1 score of 0.7481, demonstrating its robustness in identifying hateful content across linguistic variations. For Task C (Target Identification), it reached a Macro F1 score of 0.6715, highlighting its ability to classify targets into “individual,” “organization,” and “community” with high accuracy. Our work addresses the gap in Devanagari-scripted multilingual hate speech analysis and sets a benchmark for future research in low-resource language contexts.

This study explores the challenges of natural language understanding (NLU) in multilingual contexts, focusing on Devanagari-scripted languages such as Nepali, Marathi, Sanskrit, Bhojpuri, and Hindi. Language identification within these languages is complex due to their structural and lexical similarities. We present a hybrid Attention BiLSTM-XLM-RoBERTa model, achieving a state-of-the-art F1 score of 0.9974 on the test set, despite limited resources. Our model effectively distinguishes between closely related Devanagari-scripted languages, providing a solid foundation for context-aware NLU systems that enhance language-specific processing and promote inclusive digital interactions across diverse linguistic communities.

This paper focuses on detecting fake news in resource-constrained languages, particularly Malayalam. We present a novel framework combining subword tokenization, Sanskrit-transliterated Subword2vec embeddings, and a powerful Bidirectional Long Short-Term Memory (BiLSTM) architecture. Despite using only monolingual Malayalam data, our model excelled in the FakeDetect-Malayalam challenge, ranking 4th. The innovative subword tokenizer achieves a remarkable 200x compression ratio, highlighting its efficiency in minimizing model size without compromising accuracy. Our work facilitates resource-efficient deployment in diverse linguistic landscapes and sparks discussion on the potential of multilingual data augmentation. This research provides a promising avenue for mitigating linguistic challenges in the NLP-driven battle against deceptive content.

This research focuses on Homophobia and Transphobia Detection in Dravidian languages, specifically Telugu, Kannada, Tamil, and Malayalam. Leveraging the Homophobia/ Transphobia Detection dataset, we propose an innovative approach employing a custom-designed tokenizer with a Bidirectional Long Short-Term Memory (BiLSTM) architecture. Our distinctive contribution lies in a tokenizer that reduces model sizes to below 7MB, improving efficiency and addressing real-time deployment challenges. The BiLSTM implementation demonstrates significant enhancements in hate speech detection accuracy, effectively capturing linguistic nuances. Low-size models efficiently alleviate inference challenges, ensuring swift real-time detection and practical deployment. This work pioneers a framework for hate speech detection, providing insights into model size, inference speed, and real-time deployment challenges in combatting online hate speech within Dravidian languages.