SwitchLang AI: Advanced NLP for Seamless Code-Switching & Multilingual Text Processing

SwitchLang AI: Advanced NLP for Seamless Code-Switching & Multilingual Text Processing

K Swetha Sailaja*1, Devarakonda Shashikanth*2, Gardasu Sri Charan*3, Yash Vinay Chamle*4

*1 Assistant Professor of Department of CSE (AI & ML) Of ACE Engineering College, India.

*2,3,4 Students of Department of CSE (AI & ML) Of ACE Engineering College, India.

ABSTRACT

The rise of global digital communication has increased code-switching and multilingual interactions, particularly on social media, messaging apps, and online platforms. Traditional NLP systems, often trained on monolingual data, face challenges in effectively handling these complex linguistic patterns. This paper reviews recent progress in Natural Language Processing (NLP) focused on addressing multilingualism and code-switching, emphasizing advancements in models, datasets, and real-world applications. Key approaches include language-agnostic methods, transfer learning, and the use of pre-trained multilingual models such as mBERT, XLM-R, and other transformer-based innovations. These tools enable improved cross-lingual understanding and adaptability. We introduce Switchlang AI, a conceptual framework designed to support seamless multilingual processing and robust performance across diverse linguistic contexts, including both high-resource and low-resource language settings.

Keywords: Code-switching, Multilingual NLP, Pre-trained Language Models, mBERT, XLM-R, Transfer Learning, Language-Agnostic Models, Cross-Lingual Understanding, Natural Language Processing, Switchlang AI.