DELAB - IIITM WMT 25: Enhancing Low-Resource Machine Translation for M anipuri and A ssamese (2025)

Abstract: This paper describe DELAB-IIITM’s submission system for the WMT25 machine translation shared task. We participated in two sub-task of the Indic Translation Task, en↔as and en↔mn i.e. Assamese (Indo Aryan language) and Manipuri (Tibeto Burman language) with a total of six translation directions, including mn→en, mn←en, en→as, en←as, mn→as, mn←as. Our fine tuning process aims to leverages the pretrained multilingual NLLB-200 model, a machine translation model developed by Meta AI as part of the No Language Left Behind (NLLB) project, through two main development, Synthetic parallel corpus creation and Strategic Fine-tuning. The Fine-tuning process involves strict data cleaning protocols, Adafactor optimizer with low learning rate(2e-5), 2 training epochs, train-test data splits to prevent overfitting, and Seq2SeqTrainer framework. The official test data was used to generate the target language with our fine-tuned model. Experimental results show that our method improves the BLEU scores for translation of these two language pairs. These findings confirm that back-translation remains challenging, largely due to morphological complexity and limited data availability.

## introduction meiteilon (manipuri) is a tibeto-burman language spoken primarily in manipur, while assamese is an indo-aryan language spoken mainly in assam. both manipuri and assamese are recognized as official languages of india. there is a severe lack of parallel corpora and standardized digital resources. the data scarcity hinders the development of robust neural machine translation (nmt) models, as they typically require large-scale bilingual datasets for training @xcite . the morphological complexity and syntactic diversity of tibeto-burman languages such as meiteilon pose major challenges for mt systems, especially within low-resource scenarios @xcite . the neglect of low-resource languages in machine translation is exacerbated by the overwhelming focus on high-resource languages, but this problem can be mitigated through transfer learning from massively multilingual pre-trained models such as mbert @xcite and nllb @xcite . for such languages, mt systems risk perpetuating datasets and language-specific architectures @xcite . despite the challenges, researchers are actively working on improving mt for low-resource languages such as manipuri and assamese through various techniques like transfer learning, multilingual models, and back-translation @xcite @xcite . recent wmt shared tasks on low-resource indic languages translation have been significantly advancing in the field @xcite @xcite . this paper describes the fine-tuning of a pretrained multilingual nllb-200-distilled-600m model for translating manipuri to english, english to assamese, manipuri to assamese and backtranslation. back-translation, here, refers to the translation in which the translation direction is opposite to which the model is trained to perform the translation task. the layout of the subsequent paper is as follows. section 2 highlights some of the related works. section 3 describes the implementation of the proposed translation systems. finally, the conclusion and future work is drawn in section 4. ## related works transformer-based models have formed the backbone of many modern machine translation systems for low-resource indic languages @xcite . these architectures, often enhanced with monolingual pre-training, language-specific fine-tuning, and inference-time strategies like knn-mt, have demonstrated notable improvements in translation quality. for instance, @xcite observed that such enhancements consistently improved bleu scores, reinforcing the value of augmenting training with back-translation and model averaging techniques. mbart @xcite and mbart-large-50 @xcite are used in multilingual setups, where fine-tuning on filtered corpora, using semantic tools like labse @xcite ) embeddings, showed limited gains due to poor back-translation quality and the morphological complexity of the target languages @xcite . indicbart @xcite is a pre-trained bart model for indic languages, specifically trained for assamese, bengali, gujarati, hindi, marathi, odia, punjabi, kannada, malayalam, tamil, telugu and english. recently transformer-based models specialized for machine translation of indic languages like indic-trans @xcite and indictrans2 @xcite are available, which are trained on largest available indic language parallel corpora namely samanantar and bpcc respectively. indic-trans model was trained for 11 indic languages whereas indictrans2 was trained for all the 22 scheduled indian languages. nllb @xcite , a massively multilingual machine translation model has proven to be a breakthrough in the high-quality translation of around 200 languages across the world. muril (multilingual representations for indian languages) @xcite , is a multilingual language model specifically built for indic languages supporting around 17 languages. muril outperforms multilingual bert on all nlp tasks. recent advances in low-resource machine translation for indic languages were explored in the wmt 2024 shared task @xcite . ## method we participate in two sub-tasks en↔as and en↔mn with a total of six translation directions, including @xmath0, mn←en, @xmath1, en←as, @xmath2, mn←as. we generate synthetic parallel data using the pretrained model nllb-200-distilled-600m. the proposed technique includes data preparation, pre-training, fine-tuning, and model evaluation to develop the machine translation systems.