Advancing Natural Language Processing for Underrepresented Tibeto-Burman Languages in Northeast India

Natural Language Processing (NLP) plays a vital role in bridging digital divides by facilitating communication and information access across diverse linguistic communities. This paper analyses the particular difficulties and the potential directions of constructing NLP resources for the Tibeto-Burman languages that are used in the North-East India: The region is linguistically diverse; however, it does not possess large-scale language resources. Some of the special characteristics of the Tibeto-Burman languages include tonal system, morphological complexity, distinctive syntactic features and script variety which present major obstacles to NLP applications including machine translation, speech recognition and named entity identification. The lack of digital corpora and annotated datasets is another challenge which adds to the problem. Focusing on the case studies, as well as the recent trends of the field, this work describes the emerging and promising techniques like transfer learning, data augmentation, and community-driven development. These methods seek to address the issues of limited data and increase the efficiency and effectiveness of applying NLP tools to the Tubeto-Burman languages in North-East India. Ultimately, improving NLP tools in this area helps to strengthen language documentation and ensure equal access to digital resources, as well as promoting the global development of NLP field knowledge and research. Addressing these challenges can pave the way for more inclusive and effective communication technologies across diverse linguistic landscapes.