T ranssion MT ’s Submission to the I ndic MT Shared Task in WMT 2025 (2025)

Abstract: This study addresses the low-resource Indian lan- 002guage translation task (English Assamese, English Ma- 003nipuri) at WMT 2025, proposing a cross-iterative back- 004translation and data augmentation approach based on 005dual pre-trained models to enhance translation perfor- 006mance in low-resource scenarios. The research method- 007ology primarily encompasses four aspects: (1) Utilizing 008open-source pre-trained models IndicTrans2_1B and 009NLLB_3.3B, fine-tuning them on official bilingual data, 010followed by alternating back-translation and incremen- 011tal training to generate high-quality pseudo-parallel cor- 012pora and optimize model parameters through multiple 013iterations; (2) Employing the open-source semantic sim- 014ilarity model (all-mpnet-base-v2) to filter monolingual 015sentences with low semantic similarity to the test set 016from open-source corpora such as NLLB and BPCC, 017thereby improving the relevance of monolingual data 018to the task; (3) Cleaning the training data, including 019removing URL and HTML format content, eliminating 020untranslated sentences in back-translation, standardiz- 021ing symbol formats, and normalizing capitalization of 022the first letter; (4) During the model inference phase, 023combining the outputs generated by the fine-tuned In- 024dicTrans2_1B and NLLB3.3B

## introduction india boasts a rich linguistic ecosystem, yet numerous languages suffer from limited digital resources. these low-resource languages face significant challenges in the construction and application of machine translation systems. particularly, assamese and manipuri in the northeastern region not only lack parallel corpora and high-quality monolingual corpora but also exhibit large differences in linguistic structure and writing systems, posing additional difficulties for the training of neural machine translation (nmt) models. under low-resource conditions, traditional neural machine translation methods cannot fully leverage the advantages of large-scale data, resulting in limited model generalization ability and translation quality. therefore, exploring how to efficiently utilize limited bilingual and monolingual data and effectively transfer cross-lingual knowledge has become a core issue in improving the translation performance of low-resource languages. the study selects opensource indictrans2-1b and nllb-3.3b as the core translation models, combines multiple rounds of iterative back-translation to generate high-quality pseudo-parallel corpora, uses semantic similarity filtering technology to enhance the alignment between monolingual data and the task, and reduces the interference of noisy data on training through strict data cleaning and standardization operations. during the inference phase, the outputs of the two models are compared and selected, with the optimal result serving as the final translation output. this study aims to verify the effectiveness of crossmodel collaborative back-translation mechanisms, data similarity augmentation, and multi-source result fusion in low-resource translation tasks, providing reusable technical routes and empirical experience for future multilingual low-resource machine translation research. ## dataset all parallel data used in this study are derived from the official bilingual data provided by the wmt 2025 low-resource indic language translation track, covering two directions: englishassamese (en-as) and englishmanipuri (en-mni). the data scale is shown in a sampling analysis of the official test set reveals a concentrated domain distribution: healthcare accounts for 65.29%, entertainment and sports for 23.56%, and culture for 11.15%. to maximize domain consistency with the test set during the data augmentation phase, the study collects english monolingual data from the nllb open corpus, bpcc open-source dataset, and specific website crawls. the open-source semantic similarity model all-mpnet-base-v2 is used to calculate the semantic similarity between the collected data and the test set samples. sampling and filtering are performed in high-similarity data according to the language pair train val test en-as 54,000 2,000 2,000 en-mni 23,000 1,000 1,000 during the data cleaning phase, strict processing is uniformly applied to bilingual and monolingual data: removing sentences containing urls, html tags, and non-linguistic characters; eliminating samples that failed to be translated or deviated from the source language in back-translation; standardizing symbols, checking and correcting english capitalization rules for the first letter; and removing duplicate sentences and abnormally short sentences. these operations significantly reduce the proportion of noisy data and ensure that the data domain distribution is highly consistent with the official test set, providing high-quality data support for subsequent cross-iterative back-translation and model optimization. ## experimental results and analysis ## conclusion this study addresses the low-resource translation task by combining two complementary multilingual pre-trained models, indictrans2_1b and nllb_3.3b, and proposes a system construction method of one-way fine-tuning for specific translation directions and dual-model iterative backtranslation augmentation. the introduction of lora parameter-efficient fine-tuning technology on nllb_3.3b significantly reduces memory and computational costs, enabling multi-stage data augmentation under limited computing power conditions. experimental results show that: 1. one-way fine-tuning can significantly improve bleu scores in low-resource translation directions (up to +11.16 bleu), effectively reducing multilingual multi-directional interference; overall, the system method in this study fully leverages the complementary advantages of the two pre-trained models, combines parameterefficient fine-tuning and dual-model iterative backtranslation, and achieves significant bleu improvements in the wmt 2025 low-resource task, providing a feasible and efficient reference scheme for the construction of low-resource machine translation systems. future work will further explore adaptive back-translation data screening for multi-model collaboration and the introduction of multimodal auxiliary information in low-resource scenarios to break through performance bottlenecks.