The WMT26 Terminology Translation Task aims to evaluate machine translation in high-stakes, term-heavy domains (technology, finance, medicine). This year, we focus solely on document-level translation and run two tasks: (1) MT with explicit document-level dictionaries, (2) MT with bitext samples that contain the specific terms. Participants are presented with the texts in three translation directions, two of which feature mid-to-low-resourced morphologically rich languages: Spanish${\rightarrow}$Basque, English${\rightarrow}$Polish, and Traditional Chinese${\rightarrow}$English. This year, the main metrics were multiple variants of overall translation quality and terminology success rate; in line with previous shared tasks, we also compared systems with no terminology, proper dictionaries, and random dictionaries to causally analyze terminology utility. 17 teams participated in our task, submitting 21 systems to Track 1 and 18 systems to Track 2. The results show that document-level translation with explicit dictionaries is close to saturation, and the best systems nearly reach the reference texts. In contrast, for translation with sample bitexts, the spread of the systems is bigger, and the best scores are lower, highlighting the need to concentrate on terminology extraction rather than its use from an explicit source. We also evaluate the grammaticality of the generated texts in Basque and Polish and observe slight trends toward using dictionary forms of the terms and assigning the most frequent grammemes.

We describe our submission to the WMT26 Multilingual Instruction Shared Task (MIST). Our system is google/gemma-4-E4B-it (8B total / 4.5B effective parameters), fully fine-tuned via knowledge distillation from a larger teacher model (Gemma-4-31B-it) on teacher-generated responses spanning three sub-tasks and 24 languages. We find that (i) a two-judge quality-agreement filter on the distillation data provides no measurable benefit over using the teacher’s outputs unfiltered, at both LoRA and full-parameter training scale, and (ii) a task- conditional decoding strategy (using two-turn English drafting for open-ended generation and summarization, versus single-turn direct decod- ing for context-grounded question answering) significantly improves quality where applied and is harmless where it is not. Our final submission combines full-parameter distillation with this task-conditional decoding scheme.

We present ForMaT (Format-Preserving Multilingual Translation), a parallel corpus of 3,956 PDFs across 15 language pairs that preserves original layout metadata proposed for multimodal machine translation. To ensure structural diversity in the dataset, we employ K-Medoids sampling over 45 geometric features, capturing complex elements like nested tables and formulas to focus only on visually diverse PDF documents. Our evaluation reveals that current MT systems struggle with spatial grounding and geometric synchronization, often losing the link between text and its visual context. ForMaT provides a benchmark for developing layout-aware translation models that integrate visual and textual context for high-fidelity document reconstruction.

This work describes Laniqo’s submission to the constrained track of the WMT25 General MT Task. We participated in 11 translation directions. Our approach combines several techniques: fine-tuning the EuroLLM-9B-Instruct model using Contrastive Preference Optimization on a synthetic dataset, applying Retrieval-Augmented Translation with human-translated data, implementing Quality-Aware Decoding, and performing postprocessing of translations with a rule-based algorithm. We analyze the contribution of each method and report improvements at every stage of our pipeline.

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.

This paper explores Minimum Bayes Risk (MBR) decoding for self-improvement in machine translation (MT), particularly for domain adaptation and low-resource languages. We implement the self-improvement process by fine-tuning the model on its MBR-decoded forward translations. By employing COMET as the MBR utility metric, we aim to achieve the reranking of translations that better aligns with human preferences. The paper explores the iterative application of this approach and the potential need for language-specific MBR utility metrics. The results demonstrate significant enhancements in translation quality for all examined language pairs, including successful application to domain-adapted models and generalisation to low-resource settings. This highlights the potential of COMET-guided MBR for efficient MT self-improvement in various scenarios.

This submission discusses my research interests in two areas: measuring user satisfaction in goal-oriented dialogue systems and exploring the potential of multi-modal interactions. For goal-oriented dialogue systems, I focus on evaluating and enhancing user satisfaction throughout the interaction process, aiming to propose innovative strategies and address the limitations of existing evaluation techniques. Additionally, I explore the benefits of multi-modal dialogue systems, highlighting their ability to provide more natural and immersive conversations by incorporating various communication modes such as speech, text, gestures, and visuals.