GenAI4ELab/papercli-papers huggingface dataset:
Findings of WMT 2025 Shared Task on Low-resource Indic Languages Translation
Authors: Partha Pakray; Reddi Krishna; Santanu Pal; Advaitha Vetagiri; Sandeep Dash; Arnab Kumar Maji; Saralin A. Lyngdoh; Lenin Laitonjam; Anupam Jamatia; Koj Sambyo; Ajit Das; Riyanka Manna
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This study proposes the results of the lowresource Indic language translation task organized in collaboration with the Tenth Conference on Machine Translation (WMT) 2025. In this workshop, participants were required to build and develop machine translation models for the seven language pairs, which were categorized into two categories. Category 1 is moderate training data available in languages i.e English–Assamese, English–Mizo, English-Khasi, English–Manipuri and English– Nyishi. Category 2 has very limited training data available in languages, i.e English–Bodo and English–Kokborok. This task leverages the enriched IndicNE-corp1.0 dataset, which consists of an extensive collection of parallel and monilingual corpora for north eastern Indic languages. The participant results were evaluated using automatic machine translation metrics, including BLEU, TER, ROUGE-L, ChrF, and METEOR. Along with those metrics, this year’s work also includes Cosine similarity for evaluation, which captures the semantic representation of the sentence to measure the performance and accuracy of the models. This work aims to promote innovation and advancements in low-resource Indic languages.
Findings of the WMT25 Terminology Translation Task: Terminology is Useful Especially for Good MTs
Authors: Kirill Semenov; Xu Huang; Vilém Zouhar; Nathaniel Berger; Dawei Zhu; Arturo Oncevay; Pinzhen Chen
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: The WMT25 Terminology Translation Task releases new resources in high-stakes domains and investigates the capabilities of translation systems to accurately and consistently translate specialized terms. This year, we feature new domain and language coverage over previous editions, introducing two distinct tracks: (1) sentence-level translation in the information technology domain for English→German, English→Russian, and English→Spanish, and (2) document-level translation in the finance domain for English↔Traditional Chinese with a document-level one-to-many dictionary. Participants are challenged to translate texts under three modes: no terminology, proper terminology, and random terminology, allowing for a causal analysis of terminology utility. Evaluation combines overall quality, terminology accuracy, and terminology consistency. This shared task attracted broad participation, with 13 teams submitting 20 systems in Track 1 and 4 teams participating in Track 2. The results show that providing proper terminology consistently boosts both overall translation quality and term accuracy, whereas reliance on random terminology yields smaller gains. Despite the near-saturation of sentence-level benchmarks, document-level finance translation still fallsshort, indicating an urgent need for long-form evaluation and more robust metrics tailored to professional domains.
Midheind at WMT25 General Machine Translation Task
Authors: Svanhvít Lilja Ingólfsdóttir; Haukur Jónsson; Kári Steinn Adhalsteinsson; Róbert Fjölnir Birkisson; Sveinbjörn Thórdharson; Thorvaldur Páll Helgason
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: We present Midheind’s system contribution to two tasks at WMT25 – Tenth Conference on Machine Translation: The General Machine Translation Task and the WMT25 Terminology Shared Task. Erlendur is a multilingual LLM-based translation system that employs a multi-stage pipeline approach, with enhancements especially for translations from English to Icelandic. We address translation quality and grammatical accuracy challenges in current LLMs through a hybrid prompt-based approach that can benefit lower-resource language pairs. In a preparatory step, the LLM analyzes the source text and extracts key terms for lookup in an English-Icelandic dictionary. The findings of the analysis and the retrieved dictionary results are then incorporated into the translation prompt. When provided with a custom glossary, the system identifies relevant terms from the glossary and incorporates them into the translation, to ensure consistency in terminology. For longer inputs, the system maintains translation consistency by providing contextual information from preceding text chunks. Lastly, Icelandic target texts are passed through our custom-developed seq2seq language correction model (Ingólfsdóttir et al., 2023), where grammatical errors are corrected. Using this hybrid method, Erlendur delivers high-quality translations, without fine-tuning. Erlendur ranked 3rd-4th overall in the General Machine Translation Task for English-Icelandic translations, achieving the highest rank amongst all systems submitted by WMT25 participants (Kocmi et al., 2025a). Notably, in the WMT25 Terminology Shared Task, Erlendur placed 3rd in Track 1 and took first place in the more demanding Track 2 (Semenov et al., 2025).
A Preliminary Study of AI Agent Model in Machine Translation
Authors: Ahrii Kim
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: We present IR_Multi-agentMT, our submission to the WMT25 General Shared Task. The system adopts an AI-agent paradigm implemented through a multi-agent workflow, Prompt Chaining, in combination with RUBRIC-MQM, an automatic MQM-based error annotation metric. Our primary configuration follows the Translate–Postedit–Proofread paradigm, where each stage progressively enhances translation quality. We conduct a preliminary study to investigate (i) the impact of initial translation quality and (ii) the effect of enforcing explicit responses from the Postedit Agent. Our findings highlight the importance of both factors in shaping the overall performance of multi-agent translation systems.
Marco Large Translation Model at WMT2025: Transforming Translation Capability in LLMs via Quality-Aware Training and Decoding
Authors: Hao Wang; Linlong Xu; Heng Liu; Yangyang Liu; Xiaohu Zhao; Bo Zeng; Longyue Wang; Weihua Luo; Kaifu Zhang
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This paper presents the Marco-MT-Algharb system, our submission to the WMT2025 General Machine Translation Shared Task from Alibaba International Digital Commerce (AIDC). Built on a large language model (LLM) foundation, the system’s strong performance stems from novel quality-aware training and decoding techniques: (1) a two-step supervised fine-tuning (SFT) process incorporating data distillation, (2) a two-step reinforcement learning (RL) framework for preference alignment, and (3) a hybrid decoding strategy that integrates word alignment with Minimum Bayes Risk (MBR) re-ranking to improve faithfulness. These approaches jointly ensure high accuracy and robustness across diverse languages and domains. In the official human evaluation, our system secured five first‐place finishes, one second, and four third‐place results in the constrained category across the 13 directions we participated in. Notably, for the English-Chinese, our results surpassed all open/closed‐source systems.
Evaluation of QWEN-3 for English to Ukrainian Translation
Authors: Cristian Grozea; Oleg Verbitsky
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: We report the results of evaluating Qwen3 for the English-to-Ukrainian language pair of the general MT task of WMT 2025.In addition to the quantitative evaluation, we performed a qualitative evaluation, in collaboration with a native Ukrainian speaker - therefore we present an example-heavy analysis of the typical failures the LLMs still do when translating natural language, particularly into Ukrainian.We report also on the practicalities of using LLMs, such as on the difficulties of making them follow instructions, on ways to exploit the increased “smartness” of the reasoning models while simultaneously avoiding the reasoning part improperly interfering with the chain in which the LLM is just one element.
SYSTRAN @ WMT 2025 General Translation Task
Authors: Dakun Zhang; Yara Khater; Ramzi Rahli; Anna Rebollo; Josep Crego
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: We present an English-to-Japanese translationsystem built upon the EuroLLM-9B (Martinset al., 2025) model. The training process involvestwo main stages: continue pretraining(CPT) and supervised fine-tuning (SFT). Afterboth stages, we further tuned the model using adevelopment set to optimize performance. Fortraining data, we employed both basic filteringtechniques and high-quality filtering strategiesto ensure data cleanness. Additionally, we classifyboth the training data and development datainto four different domains and we train andfine-tune with domain specific prompts duringsystem training. Finally, we applied MinimumBayes Risk (MBR) decoding and paragraph-levelreranking for post-processing to enhancetranslation quality.
Shy-hunyuan-MT at WMT25 General Machine Translation Shared Task
Authors: Mao Zheng; Zheng Li; Yang Du; Bingxin Qu; Mingyang Song
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: In this paper, we present our submission to the WMT25 shared task on machine translation, for which we propose Synergy-enhanced policy optimization framework, named Shy. This novel two-phase training framework synergistically combines knowledge distillation and fusion via reinforcement learning.In the first phase, we introduce a multi-stage training framework that harnesses the complementary strengths of multiple state-of-the-art large language models to generate diverse, high-quality translation candidates. These candidates serve as pseudo-references to guide the supervised fine-tuning of our model, Hunyuan-7B, effectively distilling the collective knowledge of multiple expert systems into a single efficient model.In the second phase, we further refine the distilled model through Group Relative Policy Optimization, a reinforcement learning technique that employs a composite reward function. By calculating reward from multiple perspectives, our model ensures better alignment with human preferences and evaluation metrics.Extensive experiments across multiple language pairs demonstrate that our model Shy-hunyuan-MT yields substantial improvements in translation quality compared to baseline approaches. Notably, our framework achieves competitive performance comparable to that of state-of-the-art systems while maintaining computational efficiency through knowledge distillation and strategic ensemble.
From SALAMANDRA to SALAMANDRATA: BSC Submission for WMT25 General Machine Translation Shared Task
Authors: Javier Garcia Gilabert; Xixian Liao; Severino Da Dalt; Ella Bohman; Audrey Mash; Francesca De Luca Fornaciari; Irene Baucells; Joan Llop; Miguel Claramunt; Carlos Escolano; Maite Melero
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: In this paper, we present the SalamandraTA family of models, an improved iteration of Salamandra LLMs (Gonzalez-Agirre et al., 2025) specifically trained to achieve strong performance in translation-related tasks for 38 European languages. SalamandraTA comes in two scales: 2B and 7B parameters. For both versions, we applied the same training recipe with a first step of continual pre-training on parallel data, and a second step of supervised fine-tuning on high-quality instructions.The BSC submission to the WMT25 General Machine Translation shared task is based on the 7B variant of SalamandraTA. We first extended the model vocabulary to support the additional non-European languages included in the task. This was followed by a second phase of continual pretraining and supervised fine-tuning, carefully designed to optimize performance across all translation directions for this year’s shared task. For decoding, we employed two quality-aware strategies: Minimum Bayes Risk Decoding and Translation Reranking using Comet and Comet-kiwi.We publicly release both the 2B and 7B versions of SalamandraTA, along with the newer SalamandraTA-v2 model, on Hugging Face.
Instruction-Tuned English to Bhojpuri Neural Machine Translation Using Contrastive Preference Optimization
Authors: Kshetrimayum Boynao Singh; Deepak Kumar; Asif Ekbal
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This paper presents an English to Bhojpuri machine translation (MT) system developed for the WMT25 General MT Shared Task. Given the low-resource nature of Bhojpuri, we adopt a two-stage training pipeline: unsupervised pretraining followed by supervised fine-tuning. During pretraining, we use a 300,000-sentence corpus comprising 70% Bhojpuri monolingual data and 30% English data to establish language grounding. The fine-tuning stage utilizes 29,749 bilingual English to Bhojpuri sentence pairs (including training, validation, and test sets). To adapt the system to instruction-following scenarios, we apply a novel optimization strategy: Contrastive Preference Optimization (CPO). This technique enables the model to capture fine-grained translation preferences and maintain semantic fidelity in instruction-tuned settings. We evaluate our system across multiple metrics, demonstrating moderate performance in low-resource MT tasks, particularly in diverse domains such as literary, news, social, and speech.
SH at WMT25 General Machine Translation Task
Authors: Hayate Shiroma
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: I participated in the unconstrained track of the English-to-Japanese translation task at the WMT 2025 General Machine Translation Task.My submission leverages several large language models, all of which are trained with supervised fine-tuning, and some further optimized via preference learning.To enhance translation quality, I introduce an automatic post-editing model and perform automatic post-editing.In addition, I select the best translation from multiple candidates using Minimum Bayes Risk (MBR) decoding.For MBR decoding, I use COMET-22 and LaBSE-based cosine similarity as evaluation metrics.
Simple Test Time Scaling for Machine Translation: Kaze-MT at the WMT25 General Translation Task
Authors: Shaomu Tan
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This paper describes the Kaze-MT submission to the WMT25 General Machine Translation task (Japanese–Chinese). Our system deliberately adopts a minimalist Test-Time Scaling (TTS) pipeline with three stages—Sampling, Scoring, and Selection—while avoiding any task-specific fine-tuning, in-context exemplars, or bespoke decoding heuristics. In the sampling stage, we use the zero-shot Qwen2.5-72B-Instruct model to generate 512 candidate translations under a fixed temperature schedule designed to encourage lexical and syntactic diversity without sacrificing fluency. In the scoring stage, each candidate is evaluated by multiple reference-free quality estimation (QE) models—KIWI-22, MetricX-24 Hybrid-XXL, and Remedy-24-9B. The selection stage aggregates metric-specific rankings and chooses the candidate with the lowest mean rank, which we found more stable than averaging raw scores across heterogeneous ranges. We submit to both constrained and unconstrained tracks with minimal configuration changes. According to official preliminary results, our submissions are competitive on automatic metrics; in human evaluation, Kaze-MT falls within the 8–13 cluster, delivering performance comparable to CommandA-WMT and DeepSeek-V3 and outperforming other large LLM baselines such as Mistral-Medium and other extensively tuned MT systems.
NTTSU at WMT2025 General Translation Task
Authors: Zhang Yin; Hiroyuki Deguchi; Haruto Azami; Guanyu Ouyang; Kosei Buma; Yingyi Fu; Katsuki Chousa; Takehito Utsuro
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This paper presents the submission of NTTSU for the constrained track of the English–Japanese and Japanese–Chinese at the WMT2025 general translation task.For each translation direction, we build translation models from a large language model by combining continual pretraining, supervised fine-tuning, and preference optimization based on the translation quality and adequacy.We finally generate translations via context-aware MBR decoding to maximize translation quality and document-level consistency.
A* Decoding for Machine Translation in LLMs - SRPOL Participation in WMT2025
Authors: Adam Dobrowolski; Paweł Przewłocki; Paweł Przybysz; Marcin Szymański; Dawid Siwicki
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: SRPOL team submission to WMT2025 introduces innovative approach using A* (A-star) algorithm of decoding in EuroLLM which gives diverse set of translation hypotheses. Subsequent reranking by Comet-QE and NLLB chooses the best of the diversed hypotheses which gives significant improvement of translation quality. The A* algorithm can be applied to decoding in any LLMs or other translation models. The experiment shows that by using free, openly accessible MT models you can achieve translation quality of the best online translators and LLMs using just a PC under your desk.
In2X at WMT25 Translation Task
Authors: Lei Pang; Hanyi Mao; Quanjia Xiao; Chen Ruihan; Jingjun Zhang; Haixiao Liu; Xiangyi Li
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This paper presents the open-system submission by the In2x research team for the WMT25 General Machine Translation Shared Task. Our submission focuses on Japanese-related translation tasks, aiming to explore a generalizable paradigm for extending large language models (LLMs) to other languages. This paradigm encompasses aspects such as data construction methods and reward model design. The ultimate goal is to enable large language model systems to achieve exceptional performance in low-resource or less commonly spoken languages.
CUNI at WMT25 General Translation Task
Authors: Miroslav Hrabal; Josef Jon; Martin Popel; Ondřej Bojar
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This paper describes the CUNI submissions to the WMT25 General Translation task, namely for the English to Czech, English to Serbian, Czech to German and Czech to Ukrainian language pairs. We worked in multiple teams, each with a different approach, spanning from traditional, smaller Transformer NMT models trained on both sentence and document level, to fine-tuning LLMs using LoRA and CPO. We show that these methods are effective in improving automatic MT evaluation scores compared to the base pretrained models.
UvA-MT’s Participation in the WMT25 General Translation Shared Task
Authors: Di Wu; Yan Meng; Maya Konstantinovna Nachesa; Seth Aycock; Christof Monz
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: This paper presents UvA-MT’s submission to the WMT 2025 shared task on general machine translation, competing in the unconstrained track across all 16 translation directions. Unusually, this year we use only WMT25’s blind test set (source sentences only) to generate synthetic data for LLM training, and translations are produced using pure beam search for submission. Overall, our approach can be seen as a special variant of data distillation, motivated by two key considerations: (1) perfect domain alignment, where the training and test domains are distributionally identical; and (2) the strong teacher model, GPT-4o-mini, offers high-quality outputs as both a reliable reference and a fallback in case of mere memorization.Interestingly, the outputs of the resulting model, trained on Gemma3-12B using Best-of-N (BoN) outputs from GPT-4o-mini, outperform both original BoN outputs from GPT-4o-mini and Gemma3-12B in some high-resource languages across various metrics. We attribute this to a successful model ensemble, where the student model (Gemma3-12B) retains the strengths of the teacher (GPT-4o-mini) while implicitly avoiding their flaws.
AMI at WMT25 General Translation Task: How Low Can We Go? Finetuning Lightweight Llama Models for Low Resource Machine Translation
Authors: Atli Jasonarson; Steinthor Steingrimsson
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: We present the submission of the Árni Magnússon Institute’s team for the WMT25 General translation task. We focus on the English-Icelandic translation direction. We pre-train Llama 3.2 3B on 10B tokens of English and Icelandic texts and fine-tune on parallel corpora. Multiple translation hypotheses are produced first by the fine-tuned model, and then more hypotheses are added by that same model further tuned using contrastive preference optimization. The hypotheses are then post-processed using a grammar correction model and post-processing rules before the final translation is selected using minimum Bayes risk decoding. We found that while it is possible to generate translations of decent quality based on a lightweight model with simple approaches such as the ones we apply, our models are quite far behind the best participating systems and it would probably take somewhat larger models to reach competitive levels.
KIKIS at WMT 2025 General Translation Task
Authors: Koichi Iwakawa; Keito Kudo; Subaru Kimura; Takumi Ito; Jun Suzuki
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: We participated in the constrained English–Japanese track of the WMT 2025 General Machine Translation Task.Our system collected the outputs produced by multiple subsystems, each of which consisted of LLM-based translation and reranking models configured differently (e.g., prompting strategies and context sizes), and reranked those outputs.Each subsystem generated multiple segment-level candidates and iteratively selected the most probable one to construct the document translation.We then reranked the document-level outputs from all subsystems to obtain the final translation.For reranking, we adopted a text-based LLM reranking approach with a reasoning model to take long contexts into account.Additionally, we built a bilingual dictionary on the fly from parallel corpora to make the system more robust to rare words.
Google Translate’s Research Submission to WMT2025
Authors: Mara Finkelstein; Geza Kovacs; Isaac Caswell; Tobias Domhan; Jan-Thorsten Peter; Juraj Juraska; Markus Freitag; David Vilar
Venue: EMNLP 2025 (Proceedings of the Tenth Conference on Machine Translation)

Abstract: Large Language Models have shown impressive multilingual capabilities, where translation is one among many tasks. Google Translate’s submission to the 2025 WMT evaluation tries to research how these models behave when pushing their translation performance to the limit. Starting with the strong Gemma 3 model, we carry out supervised fine tuning on high quality, synthetically generated parallel data. Afterwards we perform an additional reinforcement learning step, with reward models based on translation metrics to push the translation capabilities even further. Controlling the combination of reward models, including reference-based and quality estimation metrics, we found that the behaviour of the model could be tailored towards a more literal or more creative translation style. Our two submissions correspond to those two models. We chose the more creative system as our primary submission, targetting a human preference for better sounding, more naturally flowing text, although at the risk of losing on the accuracy of the translation. It is an open question to find the sweet spot between these two dimensions, which certainly will depend on the specific domain to handle and user preferences.