• Kunal Nimbalkar
• Mahesh Mane
• Lomesh Waykole
• H R Kulkarni
• Pranali Sisodiya

The rapid development of neural machine translation has transformed the way languages are processed, interpreted, and communicated across digital platforms. As the volume of multilingual content increases, so does the need for translation systems that can accurately preserve meaning, structure, and contextual relationships. The research works referenced in this study collectively highlight recurring challenges associated with vocabulary expansion, segmentation strategies, error propagation, and cross-lingual consistency. These studies demonstrate that translation quality is shaped by both linguistic properties, such as lexical choice and syntactic variation, and computational factors, including model architecture, dataset diversity, and training efficiency. A central theme across the collected literature is the importance of improving translation accuracy for low-resource and morphologically complex languages. Several works emphasize the role of extended vocabulary models, adaptive tokenization, and context-aware encoding techniques in reducing ambiguity and enhancing semantic clarity. Meanwhile, analyses of translation errors reveal patterns associated with long sentences, rare words, and domain- specific text, indicating that translation quality is closely linked to input structure and dataset coverage. Research also highlights the potential of advanced transformer-based architectures to handle multi-sentence contexts, long-range dependencies, and richer linguistic cues that traditional models often fail to capture. The unified insights presented in this abstract underline the need for balanced approaches that combine linguistic understanding with computational innovation. By examining the collective findings of various studies, this work aims to provide a consolidated perspective that assists researchers, developers, and educators in designing more efficient translation systems. The results highlight emerging trends, persistent challenges, and opportunities for future exploration in multilingual natural language processing.

Neural Machine Translation (NMT) , Transformer Architecture, Linguistic Complexity , Sentence Segmentation , Vocabulary Expansion , Semantic Consistency , Contextual Encoding, Low- Resource Languages, Multilingual Processing

"Challenges In Translating Low-Resource Indian Languages", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 1, page no.290-304, January-2026, Available :http://www.jetir.org/papers/JETIRHG06030.pdf

"Challenges In Translating Low-Resource Indian Languages", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 1, page no. pp290-304, January-2026, Available at : http://www.jetir.org/papers/JETIRHG06030.pdf