BVSLP : Machine Translation Using Linguistic Embellishments for I ndic MT Shared Task 2025 (2025)

Abstract: This paper describes our submission to the Indic MT 2025 shared task, where we trained machine translation systems for five low-resource language pairs: English–Manipuri, Manipuri–English, English–Bodo, English–Assamese, and Assamese–English. To address the challenge of out-of-vocabulary errors, we introduced a Named Entity Translation module that automatically identified named entities and either translated or transliterated them into the target language. The augmented corpus produced by this module was used to fine-tune a Transformer-based neural machine translation system. Our approach, termed HEMANT (Highly Efficient Machine-Assisted Natural Translation), demonstrated consistent improvements, particularly in reducing named entity errors and improving fluency for Assamese–English and Manipuri–English. Official shared task evaluation results show that the system achieved competitive performance across all five language pairs, underscoring the effectiveness of linguistically informed preprocessing for low-resource Indic MT.

## introduction this paper presents our submission to the indicmt 2025 shared task, where we developed machine translation (mt) systems for five language pairs: english-assamese, assamese-english, english-manipuri, manipuri-english, and english-bodo. the systems follow a two-stage pipeline comprising preprocessing and neural machine translation (nmt). in preprocessing, the source text undergoes tokenization, spelling normalization, and source-side linguistic analysis, with particular focus on identifying and translating named entities into the target language to reduce out-of-vocabulary (oov) errors. the processed corpus is then used to train nmt models based on an encoder-decoder architecture, with subword segmentation applied via the sentencepiece tokenizer using the byte pair encoding (bpe) model to improve vocabulary coverage and generalization. the resulting systems integrate named entity handling with standard nmt methods, thereby enhancing translation quality across the five language pairs. ## related work research in indic machine translation has increasingly emphasized the use of multilingual pretrained models and transfer learning to overcome data scarcity. the indictrans system @xcite demonstrated the effectiveness of multilingual transformer pretraining for indian languages, providing strong baselines for indo-aryan and dravidian pairs. more recently, no language left behind (nllb-200) @xcite has scaled this paradigm further, offering pretrained models across 200 languages, including assamese, thereby enabling robust fine-tuning for low-resource indic settings. the benefits of transfer learning for lowresource machine translation have been welldocumented. @xcite showed that multi-source translation improves performance by leveraging related languages, while @xcite highlighted the effectiveness of cross-lingual transfer in low-resource neural mt. in the indic context, transfer between closely related languages such as bengali and assamese or manipuri and bodo provides an opportunity to exploit linguistic similarities for improved translation performance. subword segmentation strategies have also been an important focus. @xcite introduced byte pair encoding (bpe) as a means to mitigate out-of-vocabulary issues, while @xcite proposed the unigram language model as an alternative. more recently, @xcite , in the wmt 2023 shared task, compared segmentation schemes across lowresource languages and showed that alternative approaches can outperform bpe in certain settings. efforts to improve named entity handling in mt are relatively fewer. @xcite and @xcite demonstrated that augmenting training corpora with entity translations significantly reduces oov errors in english-braille and hindi-english systems, respectively. our work extends this line by systematically integrating a named entity translation module into the preprocessing pipeline for indic language pairs. ## evaluation we participated in the shared task by training the models on the training corpus provided by the organizers and submitted the outputs generated by the systems, using the test corpus, for official evaluation. the corresponding results provided by the organizing team are presented in ## conclusion this paper presented hemant, our submission to the wmt 2025 indic mt shared task, focusing on five low-resource language pairs. the integration of a named entity translation module reduced outof-vocabulary errors and improved translation fluency. while absolute scores remain modest, the relative improvements highlight the value of linguistically informed preprocessing.