GenAI4ELab/papercli-papers huggingface dataset:
EdinHelsOW WMT 2025 CreoleMT System Description: Improving Lusophone Creole Translation through Data Augmentation, Model Merging and LLM Post-editing
Authors: Jacqueline Rowe; Ona De Gibert; Mateusz Klimaszewski; Coleman Haley; Alexandra Birch; Yves Scherrer
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: In this work, we present our submissions to the unconstrained track of the System subtask of the WMT 2025 Creole Language Translation Shared Task. Of the 52 Creole languages included in the task, we focus on translation between English and seven Lusophone Creoles. Our approach leverages known strategies for low-resource machine translation, including back-translation and distillation of data, fine-tuning pre-trained multilingual models, and post-editing with large language models and lexicons. We also demonstrate that adding high-quality parallel Portuguese data in training, initialising Creole embeddings with Portuguese embedding weights, and strategically merging best checkpoints of different fine-tuned models all produce considerable gains in performance in certain translation directions. Our best models outperform the baselines on the Task test set for eight out of fourteen translation directions. When evaluated on a more diverse test set, they surpass the baselines in all but one direction.
KozKreolMRU WMT 2025 CreoleMT System Description: Koz Kreol: Multi-Stage Training for English–Mauritian Creole MT
Authors: Yush Rajcoomar
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: Mauritian Creole (Kreol Morisyen), spoken by approximately 1.5 million people worldwide, faces significant challenges in digital language technology due to limited computational resources. This paper presents “Koz Kreol”, a comprehensive approach to English–Mauritian Creole machine translation using a three-stage training methodology: monolingual pretraining, parallel data training, and LoRA fine-tuning. We achieve state-of-the-art results with a 28.82 BLEU score for EN→MFE translation, representing a 74% improvement over ChatGPT-4o. Our work addresses critical data scarcity through the use of existing datasets, synthetic data generation, and community-sourced translations. The methodology provides a replicable framework for other low-resource Creole languages while supporting digital inclusion and cultural preservation for the Mauritian community. This paper consists of both a systems and data subtask submission as part of a Creole MT Shared Task.
JHU WMT 2025 CreoleMT System Description: Data for Belizean Kriol and French Guianese Creole MT
Authors: Nathaniel Robinson
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This document details the Johns Hopkins University’s submission to the 2025 WMT Shared Task for Creole Language Translation. We submitted exclusively to the data subtask, contributing machine translation bitext corpora for Belizean Kriol with English translations, and French Guianese Creole with French translations. These datasets contain 5,530 and 1,671 parallel lines of text, respectively, thus amounting to an 2,300% increase in publicly available lines of bitext for Belizean Creole with English, and an 370% such increase for French Guianese Creole with French. Experiments demonstrate genre-dependent improvements on our proposed test sets when the relevant state-of-the-art model is fine-tuned on our proposed train sets, with improvements across genres of up to 33.3 chrF++.
WMT 2025 CreoleMT Systems Description : Martinican Creole and French
Authors: Ludovic Mompelat
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This paper describes our submissions to the constrained subtask of the WMT25 Creole Machine Translation shared task. We participated with a bidirectional Martinican Creole – French system. Our work explores training-time strategies tailored for low-resource MT, including LoRA fine-tuning, curriculum sampling, gradual unfreezing, and multitask learning. We report competitive results against the baseline on both translation directions.
JU-NLP: Improving Low-Resource Indic Translation System with Efficient LoRA-Based Adaptation
Authors: Priyobroto Acharya; Haranath Mondal; Dipanjan Saha; Dipankar Das; Sivaji Bandyopadhyay
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: Low-resource Indic languages such as Assamese, Manipuri, Mizo, and Bodo face persistent challenges in NMT due to limited parallel data, diverse scripts, and complex morphology. We address these issues in the WMT $2025$ shared task by introducing a unified multilingual NMT framework that combines rigorous language-specific preprocessing with parameter-efficient adaptation of large-scale models. Our pipeline integrates the NLLB-$200$ and IndicTrans$2$ architectures, fine-tuned using LoRA and DoRA, reducing trainable parameters by over 90% without degrading translation quality. A comprehensive preprocessing suite, including Unicode normalization, semantic filtering, transliteration, and noise reduction, ensures high-quality inputs, while script-aware post-processing mitigates evaluation bias from orthographic mismatches. Experiments across English-Indic directions demonstrate that NLLB-$200$ achieves superior results for Assamese, Manipuri, and Mizo, whereas IndicTrans$2$ excels in English-Bodo. Evaluated using BLEU, chrF, METEOR, ROUGE-L, and TER, our approach yields consistent improvements over baselines, underscoring the effectiveness of combining efficient fine-tuning with linguistically informed preprocessing for low-resource Indic MT.
An Attention-Based Neural Translation System for English to Bodo
Authors: Subhash Wary; Birhang Borgoyary; Akher Ahmed; Mohanji Sah; Apurbalal Senapati
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: Bodo is a resource scarce, the indigenous language belongs to the Tibeto-Burman family. It is mainly spoken in the north-east region of India. It has both linguistic and cultural significance in the region. Only a limited number of resources and tools are available in this language. This paper presents a study of neural machine translation for the English-Bodo language pair. The system is developed on a relatively small parallel corpus provided by the Low-Resource Indic Language Translation as a part of WMT-2025. The system is evaluated by the WMT-2025 organizers with the evaluation matrices like BLUE, METEOR, ROUGE-L, chrF and TER. The result is not promising but it will help for the further improvement. The result is not encouraging, but it provides a foundation for further improvement.
Tackling Low-Resource NMT with Instruction-Tuned LLaMA: A Study on Kokborok and Bodo
Authors: Deepak Kumar; Kshetrimayum Boynao Singh; Asif Ekbal
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This paper presents a new neural machine translation (NMT) system aimed at low-resource language pairs: English to Kokborok and English to Bodo. The framework leverages the LLaMA3-8B-Instruct model along with LoRA-based parameter-efficient fine-tuning. For translating into Kokborok, the model undergoes an initial continued pre-training phase on a dataset containing 75,000 Kokborok and 25,000 English monolingual sentences, followed by instruction-tuning. This tuning uses a reformulated version of WMT25 dataset, adapted to the Alpaca format to support instructional goals. In the Bodo translation, the model is pre-trained on a more extensive dataset of 350,000 Bodo and 125,000 English sentences, using a similar instruction-tuning approach. LoRA adapters are used to modify the large LLaMA3 model for these low-resource settings. Testing with the WMT25 test dataset reveals modest translation results, highlighting the difficulties in translating for low-resource languages. Translating English to Bodo, the model achieved a BLEU score of 4.38, a TER of 92.5, and a chrF score of 35.4. For English to Kokborok, it yielded scores of 5.59 in chrF, 105.4 in TER, and 0.17 in BLEU. These results underscore the intricacies of the task and highlight the critical need for further data collection, domain-specific adaptations, and improvements in model design to better support underrepresented languages.
DELAB-IIITM WMT25: Enhancing Low-Resource Machine Translation for Manipuri and Assamese
Authors: Dingku Oinam; Navanath Saharia
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

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.
Transformers: Leveraging OpenNMT and Transfer Learning for Low-Resource Indian Language Translation
Authors: Bhagyashree Wagh; Harish Bapat; Neha Gupta; Saurabh Salunkhe
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This paper describes our submission to the WMT 2025 (Pakray et al, 2025) Shared Task on Low-Resource Machine Translation for Indic languages. This task is an extension of the efforts which was originally initiated in WMT 2023 (Pal et al., 2023), and further continued to WMT 2024 (Pakray et al, 2024), received significant participation from the global community. We address English ↔ {Assamese, Bodo, Manipuri} translation, leveraging Hindi and Bengali as high-resource bridge languages. Our approach employs Transformer-based Neural Machine Translation (NMT) models, initialized through multilingual pre-training on high-resource Indic languages, followed by fine-tuning on limited parallel data for the target low-resource languages. The pre-training stage provides a strong multilingual representation space, while fine-tuning enables adaptation to specific linguistic characteristics of the target languages. We also apply consistent preprocessing, including tokenization, true casing, and subword segmentation (Sennrich et al., 2016) with Byte-Pair Encoding (BPE), to handle the morphological complexity of Indic languages. Evaluation on the shared task test sets demonstrates that pre-training followed by fine-tuning yields notable improvements over models trained solely on the target language data.
RBG-AI: Benefits of Multilingual Language Models for Low-Resource Languages
Authors: Barathi Ganesh Hb; Michal Ptaszynski
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This paper investigates how multilingual language models benefit low-resource languages through our submission to the WMT 2025 Low-Resource Indic Language Translation shared task. We explore whether languages from related families can effectively support translation for low-resource languages that were absent or underrepresented during model training. Using a quantized multilingual pretrained foundation model, we examine zero-shot translation capabilities and cross-lingual transfer effects across three language families: Tibeto-Burman, Indo-Aryan, and Austroasiatic. Our findings demonstrate that multilingual models failed to leverage linguistic similarities, particularly evidenced within the Tibeto-Burman family. The study provides insights into the practical feasibility of zero-shot translation for low-resource language settings and the role of language family relationships in multilingual model performance.
ANVITA : A Multi-pronged Approach for Enhancing Machine Translation of Extremely Low-Resource Indian Languages
Authors: Sivabhavani J; Daneshwari Kankanwadi; Abhinav Mishra; Biswajit Paul
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: India has a rich diverse linguistic landscape including 22 official languages and 122 major languages. Most of these 122 languages fall into low, extremely low resource categories and pose significant challenges in building robust machine translation system. This paper presents ANVITA Indic LR machine translation system submitted to WMT 2025 shared task on Low-Resource Indic Language Translation covering three extremely low-resource Indian languages Nyshi, Khasi, and Kokborok. A transfer learning based strategy is adopted and selected suitable public pretrained models (NLLB, ByT5), considering aspects such as language, script, tokenization and fine-tuned with the organizer provided dataset. Further, to tackle low-resource language menace better, the pretrained models are enriched with new vocabulary for improved representation of these three languages and selectively augmented data with related-language corpora, supplied by the organizer. The contrastive submissions however made use of supplementary corpora sourced from the web, generated synthetically, and drawn from proprietary data. On the WMT 2025 official test set, ANVITA achieved BLEU score of 2.41-11.59 with 2.2K to 60K corpora and 6.99-19.43 BLUE scores with augmented corpora. Overall ANVITA ranked first for {Nyishi, Kokborok}↔English and second for Khasi↔English across evaluation metrics including BLUE, METEOR, ROUGE-L, chrF and TER.
DoDS-IITPKD:Submissions to the WMT25 Low-Resource Indic Language Translation Task
Authors: Ontiwell Khongthaw; G.l. Salvin; Shrikant Budde; Abigairl Chigwededza; Dhruvadeep Malkar; Swapnil Hingmire
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: Low-resource translation for Indic languages poses significant challenges due to limited parallel corpora and linguistic diversity. In this work, we describe our participation in the WMT 2025 shared task for four Indic languages-Khasi, Mizo, Assamese, which is categorized into Category 1 and Bodo in Cate- gory 2. For our PRIMARY submission, we fine- tuned the distilled NLLB-200 model on bidi- rectional English↔Khasi and English↔Mizo data, and employed the IndicTrans2 model family for Assamese and Bodo translation. Our CONTRASTIVE submission augments training with external corpora from PMIN- DIA and Google SMOL to further enrich low- resource data coverage. Both systems lever- age Low-Rank Adaptation (LoRA) within a parameter-efficient fine-tuning framework, en- abling lightweight adapter training atop frozen pretrained weights. The translation pipeline was developed using the Hugging Face Trans- formers and PEFT libraries, augmented with bespoke preprocessing modules that append both language and domain identifiers to each instance. We evaluated our approach on par- allel corpora spanning multiple domains- ar- ticle based, newswire, scientific, and biblical texts as provided by the WMT25 dataset, under conditions of severe data scarcity. Fine-tuning lightweight LoRA adapters on targeted parallel corpora yields marked improvements in evalua- tion metrics, confirming their effectiveness for cross-domain adaptation in low-resource Indic languages.
A Preliminary Exploration of Phrase-Based SMT and Multi-BPE Segmentations through Concatenated Tokenised Corpora for Low-Resource Indian Languages
Authors: Saumitra Yadav; Manish Shrivastava
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This paper describes our methodology and findings in building Machine Translation (MT) systems for submission to the WMT 2025 Shared Task on Low-Resource Indic Language Translation. Our primary aim was to evaluate the effectiveness of a phrase-based Statistical Machine Translation (SMT) system combined with a less common subword segmentation strategy for languages with very limited parallel data. We applied multiple Byte Pair Encoding (BPE) merge operations to the parallel corpora and concatenated the outputs to improve vocabulary coverage. We built systems for the English–Nyishi, English–Khasi, and English–Assamese language pairs. Although the approach showed potential as a data augmentation method, its performance in BLEU scores was not competitive with other shared task systems. This paper outlines our system architecture, data processing pipeline, and evaluation results, and provides an analysis of the challenges, positioning our work as an exploratory benchmark for future research in this area.
AkibaNLP-TUT: Injecting Language-Specific Word-Level Noise for Low-Resource Language Translation
Authors: Shoki Hamada; Tomoyosi Akiba; Hajime Tsukada
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: In this paper, we describes our system for the WMT 2025 Low-Resource Indic Language Translation Shared Task.The language directions addressed are Assamese↔English and Manipuri→English.We propose a method to improve translation performance from low-resource languages (LRLs) to English by injecting Language-specific word-level noise into the parallel corpus of a closely related high-resource language (HRL).In the proposed method, word replacements are performed based on edit distance, using vocabulary and frequency information extracted from an LRL monolingual corpus.Experiments conducted on Assamese and Manipuri show that, in the absence of LRL parallel data, the proposed method outperforms both the w/o noise setting and existing approaches. Furthermore, we confirmed that increasing the size of the monolingual corpus used for noise injection leads to improved translation performance.
BVSLP: Machine Translation Using Linguistic Embellishments for IndicMT Shared Task 2025
Authors: Nisheeth Joshi; Palak Arora; Anju Krishnia; Riya Lonchenpa; Mhasilenuo Vizo
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

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.
TranssionMT’s Submission to the Indic MT Shared Task in WMT 2025
Authors: Zebiao Zhou; Hui Li; Xiangxun Zhu; Kangzhen Liu
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

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
Laniqo at WMT25 Terminology Translation Task: A Multi-Objective Reranking Strategy for Terminology-Aware Translation via Pareto-Optimal Decoding
Authors: Kamil Guttmann; Adrian Charkiewicz; Zofia Rostek; Mikołaj Pokrywka; Artur Nowakowski
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This paper describes the Laniqo system submitted to the WMT25 Terminology Translation Task. Our approach uses a Large Language Model fine-tuned on parallel data augmented with source-side terminology constraints. To select the final translation from a set of generated candidates, we introduce Pareto-Optimal Decoding - a multi-objective reranking strategy. This method balances translation quality with term accuracy by leveraging several quality estimation metrics alongside Term Success Rate (TSR). Our system achieves TSR greater than 0.99 across all language pairs on the Shared Task testset, demonstrating the effectiveness of the proposed approach.
Fine-tuning NMT Models and LLMs for Specialised EN-ES Translation Using Aligned Corpora, Glossaries, and Synthetic Data: MULTITAN at WMT25 Terminology Shared Task
Authors: Lichao Zhu; Maria Zimina-Poirot; Cristian Valdez; Stephane Patin
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This paper describes our participation in the WMT25 Terminology Shared Task, specifically Track 1 (Spanish to English) focused on translation within the Information Technology (IT) domain. The shared task challenges participants to improve machine translation systems by effectively incorporating terminology constraints to ensure accurate and consistent translation of specialised technical terms. We experimented with several approaches to tackle terminology and lexical constraints with both NMT systems and LLMs with a small amount of training data and a glossary. Experimental results demonstrate that systems behave differently with and without glossary. The NMT system seems rather limited in adapting to special lexicon and resizing embeddings, which is the opposite of the case with LLMs preferring structured instructions. Through this participation, our objective is to improve terminology accuracy and overall translation quality, highlight the potential of specialised terminology-aware translation models for technical domains, and explore possibilities of fine-tuning of LLMs and NMT models with domain and lexical constraints.