Effective communication in asylum reception settings requires reliable machine translation (MT) across many languages, including low-resource ones. Using data from the ANON project, we compare retrieval-augmented LLM translation with adaptive Neural MT across 14 target languages with varying resource levels. Working with a very small translation memory of only 358 sentences, we evaluate fuzzy match (FM) augmentation as an in-context learning strategy for open-source and commercial LLMs and benchmark these against ModernMT with and without domain adaptation. In the LLM setting, FM-based example selection consistently outperforms random selection and zero-shot prompting, with the largest gains for low-resource languages. Adaptive NMT retains an overall advantage, although Gemini~Pro approaches its performance and outperforms it on 6 of 14 languages, highlighting a trade-off between translation quality and data sovereignty in privacy-sensitive contexts. These findings show that FM augmentation remains effective under severe data constraints and emphasise the importance of language-specific evaluation in multilingual MT.

Large Language Models (LLMs) have recently demonstrated strong performance in machine translation (MT). However, most prior work focuses on improving or benchmarking translation quality, offering limited insight into when and why LLM-based translation fails. In this work, we systematically analyze failure modes of LLMs in MT by evaluating 15 models, including four reasoning LLMs, across 22 language pairs (LPs) with varying resource levels. We find that non-English-centric LPs consistently yield lower COMET scores than English-centric pairs. To investigate the underlying causes, we introduce Token Activation Rate (TAR), a metric that captures how effectively a model utilizes language-specific tokens in its vocabulary during generation. We validate TAR as a proxy for language representation using models with known language distributions in the training data, and show that lower TAR is strongly associated with poorer translation performance. Furthermore, reasoning LLMs tend to generate more tokens when translating into low-TAR languages, suggesting a compensatory mechanism, although its impact on translation quality varies across models. Overall, our findings emphasize the importance of token-level dynamics in understanding MT performance of LLMs.

Timely and reliable multilingual communication is critical during natural and human-induced disasters, but developing effective solutions for crisis communication is limited by the scarcity of curated parallel data. We propose a domain-adaptive pipeline that expands a small reference corpus, by retrieving and filtering data from general corpora. We use the resulting dataset to fine-tune a small language model for crisis-domain translation and then apply preference optimization to bias outputs toward CEFR A2-level English. Automatic and human evaluation shows that this approach improves readability, while maintaining strong adequacy. Our results indicate that simplified English, combined with domain adaptation, can function as a practical lingua franca for emergency communication when full multilingual coverage is not feasible.

Generative AI tools, such as ChatGPT, are applied to a wide range of languagerelated tasks, including translation. Despite their current popularity among users and researchers and the impressive results obtained on several benchmarks (Kocmi et al., 2024a; Deutsch et al., 2025), their potential side-effects on languages and translations are still understudied (Vanmassenhove, 2025). The paradigm shift from Machine Translation (MT) to Generative AI Translation (GAIT) likely calls for a reconsideration of our assessment and evaluation metrics and practices. In this work, we focus on GAIT by analyzing translations from four multilingual large language models (MLLMs), mBART, Jamba-1.5-large, GPT 4o and DeepSeek R1 applied to three different domains (news, literature and poetry) for the English-Dutch language pair. Focusing on metrics related to lexical and textual diversity, we find that while GAIT text for literature if of significantly high lexical and grammatical richness, that is not the case for news and poetry. We also assess the homogeneity of AI-generated text through a set of clustering and classification experiments. In addition to a clear separation between human- and AI-generated content, our results indicate that GAIT output is more homogeneous among MLLMs.

We propose an LLM-as-a-Jury framework for determining machine translation publishability, aggregating judgments from multiple large language models via logistic regression rather than relying on a single judge. Publishability is defined as the absence of major or critical errors—those that render a translation unsuitable for public release without human post-editing. We compare three evaluation frameworks: a generic Edit Effort Estimation (EEE) prompt based on lexical accuracy, grammatical correctness and semantic coherence, a generic Linguistic Quality Assurance (LQA) prompt based on the MQM error taxonomy, and a purpose-built Publishability prompt optimized via DSPy and augmented with domain-specific fine-tuning. Experiments across three domains and nine language pairs show that (i) the jury ensemble matches or outperforms the best individual juror in nearly every condition, (ii) EEE and LQA juries are competitive with and occasionally exceed the Publishability jury on macro-F1, (iii) the Publishability framework offers stronger precision and a more favorable error correction asymmetry, and (iv) domain-specific fine-tuning yields substantial recall gains in client-heavy domains. These results support the viability of fully automated publishability determination in enterprise MT workflows.

LLM performance is highly sensitive to prompt design, yet whether automatic prompt optimization can replace expert prompt engineering in linguistic tasks remains unexplored. We present the first systematic comparison of hand-crafted zero-shot expert prompts, base DSPy signatures, and GEPA-optimized DSPy signatures across translation, terminology insertion, and language quality assessment, evaluating five model configurations. Results are task-dependent. In terminology insertion, optimized and manual prompts produce mostly statistically indistinguishable quality. In translation, each approach wins on different models. In LQA, expert prompts achieve stronger error detection while optimization improves characterization. Across all tasks, GEPA elevates minimal DSPy signatures, and the majority of expert–optimized comparisons show no statistically significant difference. We note that the comparison is asymmetric: GEPA optimization searches programmatically over gold-standard splits, whereas expert prompts require in principle no labeled data, relying instead on domain expertise and iterative refinement.

Current state-of-the-art Quality Estimation (QE) in machine translation relies on massive, proprietary LLMs, raising data privacy concerns. We demonstrate that smaller, open-source LLMs (<30B parameters) are a viable, cost-effective and privacy-preserving alternative. Using a single-pass prompting strategy, our models simultaneously generate quality scores, MQM error annotations, suggested error corrections, and full post-editions. Our analysis shows these models achieve highly competitive system-level correlations with human judgments that outperform traditional neural metrics, fine-tuned models, and human inter-annotator agreement, effectively approximating the capabilities of much larger proprietary LLMs.

Code-mixed and Romanized texts are widely used in digital content, yet they remain largely underexplored for many low-resource languages, including Sinhala. The scarcity of high-quality parallel data has limited progress on downstream tasks, such as machine translation and transliteration. We introduce SinMix2Mono, the largest manually annotated parallel training dataset, followed by the first gold standard benchmark and code-mixed transliteration ambiguity corpora for code-mixed romanized Sinhala to Sinhala conversion. The dataset comprises approximately 25,000 real-world sentences collected from social media, covering diverse domains and authentic code-mixing patterns. To ensure high-quality translations, we used an annotation pipeline that combined rule-based transliteration, LLM-assisted translation, and human validation. The golden test dataset, which includes 2549 sentences, and the code-mixed transliteration ambiguity test were validated by three annotators, yielding Gwet’s AC1 scores of 0.7465 and 0.7068, respectively. We benchmarked nine systems, including statistical, neural and commercial LLMs. SinMix2Mono provides a robust training and evaluation resource, establishing a strong benchmark for future research on Sinhala code-mixed translation and transliteration.

Sentence-level alignment systems have been developed and evaluated primarily on parallel data, leaving their behaviour across the broader parallel-comparable spectrum of real web content poorly understood. We present a stratified empirical study of alignment quality for Catalan-English using 300 document pairs across three parallelism bands defined by mean-max LaBSE cosine similarity. We compare four systems: a hierarchical alignment pipeline (DocAlign), an ablation with paragraph pre-filtering disabled (DocAlign-NoFilter), the flat aligner Vecalign, and a flat LaBSE greedy baseline. Evaluation uses human-annotated sentence pairs and coverage-weighted quality. Quality degrades at different rates by system type: hierarchical systems maintain usable-pair rates ranging from 25% to 51% on comparable data while flat systems collapse to 2-7%. Paragraph pre-filtering reduces output volume on comparable data while raising pair quality relative to the unfiltered ablation. Vecalign is statistically indistinguishable from the greedy baseline at all parallelism levels, suggesting that LaBSE embedding discrimination is the binding constraint on flat alignment quality. Failure mode analysis of 550 low-rated pairs identifies topical mismatch as the dominant failure mode, with structural noise concentrated in flat systems.

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.

Most machine translation datasets come with a certain level of noise, and strategies for handling such data need to be robust and efficient. Data selection and filtering are challenging and may depend on expensive language-specific tools that are not necessarily available, especially for low-resource languages. This paper looks at training strategies that combine cheap heuristic filters with curriculum learning to implement iterative procedures that robustly operate on raw noisy data without expensive prior preprocessing and data selection. The intuition is that we can cluster data into buckets with varying noise levels and use different sets of buckets at different stages of MT model training. We test various strategies and compare them to pre-filtering approaches for a diverse set of low-resource languages and conclude that curriculum learning can improve robustness but does not necessarily lead to improved translation performance. Overall, the experiments demonstrate the importance of proper experimental workflows, which cannot easily generalize from one language pair and scenario to another.

This study investigates the presence and mitigation of gender bias in English-Ukrainian machine translation (MT) models. We focused on the transfer of gender bias in two English-Ukrainian MT models, using sentences that contained professional occupation names. We evaluated two gender bias mitigation methods: 1) gender tagging of source sentences, and 2) gender bias correction by Lapa LLM, a Ukrainian large language model (LLM), using a dataset that we curated for our evaluation. Our results showed that both zero-shot models contained English-Ukrainian gender bias transfer, particularly for gender-stereotypical occupations. The gender tagging mitigation method demonstrated changes to the gender assignment in our data; however, these changes led to mixed results in gender bias correction. The Lapa LLM-correction method had promising results in demonstrating a considerable mitigation of bias in our evaluation set. Overall, our study contributes a framework for the evaluation of gender bias in English-Ukrainian translation that could potentially be applied to translation pairs in other languages.

Discourse-level translation remains a major challenge for machine translation (MT) systems, particularly for translation from Indian languages to English. This difficulty arises due to factors such as rich morphology, diverse syntactic structures, unmarked gender distinctions in pronouns, and the limited availability of discourse-aware training data. Existing evaluation benchmarks primarily focus on sentence-level translation quality and fail to capture important discourse phenomena such as pronoun resolution and lexical cohesion. To address this gap, we introduce IndicDISCO-MT, a parallel benchmark dataset covering translations from eight Indian languages such as Bengali, Gujarati, Hindi, Marathi, Kannada, Tamil, Telugu and Urdu to English. On top of this dataset, we are first to introduce DiscoAlign, a human annotated word to word alignment benchmark dataset, that captures correspondences between source and target words across languages. In addition, we propose two evaluation benchmarks, ProAlign and LexiAlign, designed to specifically assess the ability of large language models (LLMs) and MT systems to handle personal pronouns and lexical cohesion. Our evaluation of recent LLMs and MT systems on these benchmarks shows that although models achieve high overall translation quality, they still struggle to accurately preserve discourse-level phenomena. The proposed benchmarks provide a systematic framework for evaluating discourse aware translation and can facilitate the development of MT systems that generate more coherent and contextually consistent translations.

As machine translation increasingly requires deeper contextual, linguistic, and cultural understanding, multi-agent collaboration has emerged as a promising approach. Multi-agent debate (MAD) frameworks, in which multiple agents deliberate to produce a final output, have shown strong performance on objective tasks, but remain underexplored in translation, where multiple valid renderings often exist. We adapt three MAD frameworks for English-Japanese translation and evaluate them against strong generative baselines, reasoning-capable LLMs, and a prompt-based self-reflection baseline. Across general-domain and culturally grounded datasets, the Society of Mind (SoM) variant yields the strongest results in the English-to-Japanese direction, showing that zero-shot translations leave substantial room for improvement through structured deliberation. Yet the gains of debate are front-loaded: later rounds do not reliably improve quality and often reintroduce translation errors. Diagnostic and error-span analyses show that hand-designed debate protocols tend to over-revise already strong translations, leading to semantic drift and process-induced degradation. These findings highlight both the promise and the limitations of agentic translation, and suggest that effective debate-based systems require mechanisms for preserving strong intermediate outputs.

Literary translation is a difficult task that not only requires semantic accuracy but also stylistic richness and lexical diversity. Pretrained and supervised fine-tuned Large Language Models (LLMs) can over-rely on safe vocabulary choices, leading to translations that lack lexical variety. To address this problem, we propose a novel diversity-aware multi-objective Group Relative Policy Optimization (GRPO) framework that pushes the limits of open-source translation quality while increasing lexical diversity. We introduce two diversity-aware reward mechanisms, a Leave-One-Out (LOO) marginal contribution reward and a Self-BLEU penalty, balanced alongside neural quality metrics (COMET), lexical overlap (BLEU), and structural constraints. Through experiments on Turkish-English and German-English using Qwen3-14B, we show that our diversity-aware reinforcement learning approach successfully enhances lexical richness alongside translation quality. Our models achieve state-of-the-art open-source performance in literary translation, bridging the gap with leading commercial systems and demonstrating that policy optimization can effectively steer LLMs toward high-quality, lexically diverse outputs.

Machine translation (MT) systems continue to produce gender-biased translations. In a time where self-expression is paramount, mistranslations based on default behaviour and stereotyping can lead to harm for users of these systems. To better understand how these systems translate gender in the absence of clear gender cues, we need benchmarking resources that reflect gender ambiguous scenarios in a natural way. To this end, we present GAND, a gender ambiguous natural data benchmarking resource for MT consisting of English source sentences, specifically designed to analyse the influence of contextual cues on gender in translation. We leverage GAND to conduct an interpretability analysis: we translate a subset of GAND into two grammatical gender languages and extend these with manually crafted contrastive translations. A following feature attribution analysis reveals source words in context that inform the gender translation of an ambiguous referent entity in the target translation. As a newly introduced resource, GAND is designed to be benchmarked across diverse target languages and evaluated with a wide range of MT systems.

Recent advances in neural machine translation (MT) have spurred increased interest in evaluating translations beyond the sentence level, making it possible to assess discourse-level phenomena related to coherence and consistency. While existing metrics can be applied to multi-sentence spans, it remains unclear whether their scores truly capture document-level quality. We introduce MetaDocEval, an automatic contrastive test set for evaluating MT metrics across three language pairs (en–fr, en–es, en–de) when applied at the document-level. It targets a range of discourse-level phenomena and potential problems linked to translation at the document level. To evaluate how metrics behave as a function of context size, we apply them under a sliding-window protocol, varying the input from single sentences up to full documents. Our experiments show that no current metric genuinely captures document-level coherence: reference-based metrics overfit lexical overlap, reference+source metrics gain little from added context, reference-free encoders show brief context sensitivity before degrading on longer spans, and LLM-based scorers collapse beyond short inputs. A key finding is that reference access can be actively harmful for detecting discourse-level errors. Using short windows (≈ 3 sentences) offers the best trade-off between discourse error detection and score dilution.

EU legislation is equally authentic and legally binding in all 24 official languages, rendering high-quality translation a legal obligation rather than a mere choice. Therefore, high-quality language technology supporting translation processes in all EU languages are essential for language professionals at the European Parliament (EP). This paper investigates whether domain-specific fine-tuning of an open-weight Large Language Model (LLM) yields consistently larger quality gains on legislative text compared to generic text, in all 23 EU target languages from English. We evaluate ten experimental conditions: base model, in-domain and cross-domain fine-tuning, sequential generic-then-legislative fine-tuning, and zero-shot Claude Sonnet 4.6 as a proprietary reference. We analyse BLEU, chrF, TER, and COMET metrics on nearly 700,000 segments. Results confirm the hypothesis for all 23 languages: legislative fine-tuning enhances BLEU by +12.30 compared to +7.10 for generic fine-tuning, demonstrating a consistent advantage of +5.20 BLEU in all the metrics. The fine-tuned EuroLLM-22B decisively outperforms Claude Sonnet 4.6, Anthropic’s latest frontier model, on both domains, highlighting that targeted adaptation of a smaller open-weight model can surpass a state-of-the-art proprietary system. Cross-domain transfer within institutional domain is positive for all languages, with no catastrophic forgetting. Low-resource languages such as Irish and Maltese benefit the most from fine-tuning, while a divergence between BLEU and COMET rankings for some languages underlines the need of evaluation metrics alongside traditional measures.

As state-of-the-art machine translation models saturate standard benchmarks, the field needs more challenging evaluations to distinguish between models of varying quality. We propose augmenting existing benchmarks to increase translation difficulty by combining adversarial optimization with a differentiable translation difficulty estimator. Our Adversarial Translation Optimization (ATO) uses gradients from a combined difficulty and fluency objective to iteratively replace tokens. Because each step branches over candidate substitutions at every position, optimization becomes a tree search problem, which we address with Beam Search. ATO offers a gradient-based alternative to LLM-based dataset creation without LLM prompting, expensive human curation, or task-specific model training. Our ATO-modified benchmark lowers average translation quality (xCOMET) from 0.93 to 0.82, compared to 0.88 for paraphrasing and 0.86 for a zero-shot baseline. Through human evaluation we confirm that the modified texts remain reasonably natural while being substantially harder to translate. We release two datasets of 200 English texts each, generated by our methods, as well as the code.

Human evaluation of MT is essential but exhibits substantial annotator variability that limits evaluation reliability and super- vised learning. Rather than treating dis- agreement as noise or correcting it through protocol changes, we analyze its structure via learned severity classifiers. Across training regimes defined by base- line model reproducibility, we observe in- ternally coherent but mutually incompati- ble severity mappings: models trained on one regime produce confident predictions within that regime but reduced separability on the other. Margin–correctness analysis shows that instability is not uniformly low confidence; separability depends on align- ment between model-internalized and hu- man annotation regimes. These results indicate that unstable MT evaluation regions arise primarily from competing severity interpretations rather than intrinsic example difficulty. Model– annotator disagreement therefore provides a practical signal for identifying unstable evaluation regions during MT evaluation.

Term injection in machine translation is undergoing a paradigm shift in the era of large language models (LLMs). Although recent shared-task results suggest near-saturation for sentence-level term injection from pre-defined glossaries, it remains unclear whether this also holds in more challenging settings. We address this question with a custom test set for legal translation from Italian into South Tyrolean German, a non-dominant and under-resourced language variety. We cover three terminology challenges: simple term injection, localisation of abbreviated forms, and homonym disambiguation. We focus on Reasoning Models (RMs) leveraging Test-Time Scaling, comparing them with different architectures and contributing a human analysis of reasoning traces. We find that reasoning offers little benefit for simple term insertion, but yields clear gains for semantically complex cases such as homonym disambiguation. However, human evaluation of reasoning traces shows that these gains do not necessarily reflect robust and factually grounded translation-specific reasoning. We further show that without external terminological resources, even state-of-the-art RMs struggle to retrieve correct terminology for a non-dominant variety, while NMT small models remain competitive when trained on in-domain bilingual corpora. Based on these findings, we propose data collection strategies for inducing translation-specific reasoning, frameworks for adapting to and evaluating terminology across many language varieties, and terminology challenges beyond simple term injection.

Preserving affective nuance remains a challenge in Machine Translation (MT), where semantic equivalence often takes precedence over emotional fidelity. This paper evaluates the performance of three state-of-the-art Small Language Models (SLMs) – EuroLLM, Aya Expanse, and Gemma – in maintaining fine-grained emotions during backtranslation. Using the GoEmotions dataset, which comprises Reddit comments across 28 distinct categories, we assess emotional preservation across five European languages: German, French, Spanish, Italian, and Polish. Specifically, we investigate (i) the inherent capability of these SLMs to retain emotional sentiment, (ii) the efficacy of emotion-aware prompting in improving preservation, and (iii) the performance of ModernBERT as a contemporary alternative to BERT for emotion classification in MT evaluation.

Adapting large language models to specialized domains remains challenging due to the computational cost of full finetuning and the limited availability of domain-specific parallel data. We present a systematic framework for parameter-efficient domain adaptation using Low-Rank Adaptation (LoRA) geared towards efficient learning in low-resource scenarios. Our method combines data-scaling analysis, dual-track hyperparameter optimization, and competitive benchmarking. We evaluate our approach on the low-resource English–Norwegian petroleum translation domain using a distilled version of NLLB and parallel data from the Norwegian Petroleum Directorate. Our adapted model achieves 61.48 BLEU (+24.62 over the base model) and 0.9298 COMET, while updating <0.4% of parameters. Our results provide a reproducible and computationally efficient blueprint for domain adaptation in neural machine translation, particularly for specialized and resource-constrained domains.

Knowledge distillation (KD) compresses large teacher models into smaller student models by transferring soft labels or intermediate activations. While effective in general domains, KD alone falls short in specialised machine translation (MT) settings, such as biomedical translation. The student inherits only the teacher’s compressed knowledge and lacks access to external domain information. Moreover, standard KD typically relies on abundant parallel data, which is often unavailable in domain-specific scenarios. To address these limitations, we combine KD with retrieval-augmented generation (RAG) in a few-shot setting. We propose a retrieval-augmented enhanced few-shot KD framework for French-to-English biomedical translation task. The student learns to retrieve relevant in-domain knowledge from an external database, complementing the teacher’s supervision. We design and compare several retrieval strategies to enhance student capacity. Experiments show that with our terminology-aware retrieval-based methods, the student achieves performance comparable to or better than the teacher, while preserving translation quality and efficiency.

Conventional retrieval-augmented neural machine translation (RANMT) systems leverage bilingual corpora, e.g., translation memories (TMs). Yet, in many settings, monolingual corpora in the target language are often available. This work explores ways to take advantage of such resources by directly retrieving relevant target language segments, based on a source-side query. For this, we design improved cross-lingual retrieval systems, trained with both sentence level and word-level matching objectives. In our experiments with two RANMT architectures, we assess of such cross-lingual objectives in a controlled setting, reaching performances that match those of standard TM-based models. We also showcase our method on real-world settings, using much larger monolingual corpora, and observe strong improvements over both the baseline setting, and general-purpose cross-lingual retrievers.

We present an evaluation of several state-of-the-art machine translation systems supporting terminology constraints in the English–Finnish translation direction. We first perform a meta-evaluation, in which we critically evaluate the evaluation metrics we use, including the questions asked from human evaluators and the automatic evaluation methods. We find that common metrics such as term accuracy and TERm do not agree with the human evaluators’ judgement on the correctness of the terms, while LLM-as-a-judge shows promise even though it does not agree with the human evaluators on all questions. We then compare the evaluated systems based on the human evaluation results, LLM-as-a-judge, COMET, and chrF2. We find that of the systems considered, soft constraint methods, including a term-trained model and an LLM, perform better than hard constraints forced using a constrained beam search.

Evaluating the translation of multi-word expressions (MWEs) remains a major challenge for Machine Translation (MT), particularly in audiovisual subtitling, where idiomatic meaning and cultural context are essential for adequacy. This study investigates both the ability of state-of-the-art MT systems to translate Spanish MWEs into English and the extent to which current automatic evaluation methods reflect expert human judgment. We introduce ALMO-MWE, a dataset of 235 MWEs extracted from four films by Pedro Almodóvar to evaluate four MT systems using automatic metrics, LLM-as-a-judge approaches, and professional human assessment. Our results reveal a substantial mismatch between traditional automatic metrics and human judgments: n-gram-based metrics show near-zero correlation with expert evaluation and only limited discriminative capacity. In contrast, neural metrics and LLM-based judges exhibit substantially stronger alignment with human assessments, with GPT-OSS achieving the highest overall correlation. These findings highlight fundamental limitations of surface-form metrics for culturally and contextually sensitive translation phenomena and underscore the need for context-aware evaluation frameworks when assessing the translation quality of MWEs in audiovisual translation.

User-generated content (UGC) is characterised by frequent use of non-standard language, from spelling errors to expressive choices such as slang, character repetitions, and emojis. This makes evaluating UGC translation challenging: what counts as a "good" translation depends on the desired standardness level of the output. To explore this, we examine the human translation guidelines of four UGC datasets, and derive a taxonomy of twelve non-standard phenomena and five translation actions (NORMALISE, COPY, TRANSFER, OMIT, CENSOR). Our analysis reveals notable differences in how UGC is treated, resulting in a spectrum of standardness in reference translations. We show that translation scores of large language models are highly sensitive to prompts with explicit UGC translation instructions, and that they improve when they align with the dataset guidelines. We argue that fair evaluation requires both models and metrics to be aware of translation guidelines. Finally, we call for clear guidelines during dataset creation and for the development of controllable, guideline-aware evaluation frameworks for UGC translation.

Automatic Post-Editing (APE) is a widely studied approach for enhancing the output quality of Neural Machine Translation (NMT) systems. While most prior work has focused on general-purpose APE, the potential of domain-specific APE, such as for personalized or specialized content, remains underexplored due to the scarcity of domain-labeled training data. In this work, we investigate domain adaptation for APE using adapter-based methods. Our proposed multitask learning-based domain adaptation framework includes the use of a domain classifier to get a weighted combination of parallel domain-specific adapters at inference time, without requiring prior domain knowledge. This design allows the model to leverage cross-domain similarities, making it especially robust in low-resource domain scenarios. Our experimental results on English–German, English–Marathi, and English–Tamil pairs across different domains for each pair show substantial improvements over their respective general-purpose APE baselines. To facilitate further research, we will release human-annotated domain labels for triplets in WMT22 English–Marathi, and WMT24 English–Tamil APE datasets and the code.

We present two open-weight translation models for Estonian and its low-resource "relatives" in the Finno-Ugric language family. The training data includes 12 languages paired with Estonian as well as 23 more Finno-Ugric languages and varieties, ranging from mid-resource examples with tens of thousands of speakers to extremely low-resource critically endangered languages with less than a hundred speakers. The translation models use Unbabel Tower+ 2B and 9B as their starting point. We compare their performance on two benchmarks to DeepL and GPT-5.2 and show that in most cases we surpass the quality of DeepL and match or nearly match the quality of GPT-5.2’s output with just a fraction of the parameters. Among other contributions we also restore the paragraph structure of a massive synthetic multiparallel corpus for Estonian translation and use it in training the models. The resulting models, training scripts and training data are released openly (to be made public upon de-anonymization).

E-commerce product titles often include redundant information that negatively impacts the user experience. Removing repeated words through restructuring and paraphrasing can make titles more concise and improve readability. While large language models can optimize titles, their computational cost makes them impractical for large-scale applications. In this paper, we first analyze the sources of repetition in multilingual product titles, then present LocRegen, a system that uses smaller language models to efficiently remove redundancies while preserving essential product attributes. Our experiments across five languages show that LocRegen with a 7B model substantially outperforms a 47B mixture-of-experts model: LocRegen achieves a 2.4% redundant title rate compared to 3.5% for the 47B model, and maintains a 3.8% overall error rate across all error categories including key product attribute omission compared to 8.4% for the 47B model. These results demonstrate that LocRegen delivers superior performance on cost-effective hardware with acceptable latency, making it practical for large-scale deployment where much larger models would be computationally prohibitive.

In-context learning is a method for improving machine translation in Large Language Models, but its performance is sensitive to the quality of the few-shot example selection. Current retrieval strategies use semantic similarity by computing sentence embeddings, and these methods often require significant computational overhead and specialised expertise. We evaluate the impact of retrieval strategies on translation performance in a specialised domain, comparing traditional, token-based fuzzy matching against semantic sentence embeddings. We use a medical corpus from the European Medicines Agency (EMEA) for the English-Romanian and English-German language pairs, and we evaluate translation quality with automatic metrics and manual evaluation. Our results show that 1-shot and 5-shot prompting significantly outperforms the 0-shot baselines for quality in automatic evaluations for both language pairs, and in manual evaluation for English-German. For the English-Romanian pair, the average scores of the manual evaluation for both quality and ranking follow the same trend, but with no statistical significance. Token-based fuzzy matching overwhelmingly has higher automatic quality scores than embedding-based retrieval.

This study investigates whether professional translators can reliably identify short stories generated in Italian by artificial intelligence (AI) without prior specialized training. Sixty-nine translators took part in an in-person experiment, where they assessed three anonymized short stories - two written by ChatGPT-4o and one by a human author. For each story, participants rated the likelihood of AI authorship and provided justifications for their choices. While average results were inconclusive, a statistically significant subset (16.2%) successfully distinguished the synthetic texts from the human text, suggesting that their judgements were informed by analytical skill rather than chance. However, a nearly equal number misclassified the texts in the opposite direction, often relying on subjective impressions rather than objective markers, possibly reflecting a reader preference for AI-generated texts. Low burstiness and narrative contradiction emerged as the most reliable indicators of synthetic authorship, with unexpected calques, semantic loans and syntactic transfer from English also reported. In contrast, features such as grammatical accuracy and emotional tone frequently led to misclassification. These findings raise questions about the role and scope of synthetic-text editing in professional contexts.

Translators at the Canadian Parliament currently have access to a neural machine translation system as an optional tool integrated into their translation environment, whose output they can use for postediting (rather than translating from scratch); this provides a valuable opportunity to study the dynamics of MT adoption in professional settings. We report on a user study that investigates how and why translators choose to interact with this tool. Using a mixed-methods approach, we examined both human and technical factors that influence the adoption or non-adoption of the system. Drawing on our findings, we advocate for a user-centred approach to MT integration within professional translation workflows.

Building on our previous work, this paper develops practical, low-barrier methods for freelance translators and smaller language service providers to evaluate translation technologies using rigorous yet accessible analytic methods. Here we address a high-stakes, specialized need: offline translation for confidentiality-sensitive domains in which privacy constraints preclude the use of cloud-based engines and commercial LLMs. We expand the Reeve Foundation Trilingual Corpus (RFTC) used in our previous work into a multilingual corpus (RFMC) by adding sentence-aligned German and Simplified Chinese reference translations. We then benchmark several locally runnable language models (via Ollama) across four language directions on 1000+ sentences selected from this corpus. We use consistent single-prompt calls without fine-tuning or domain adaptation, comparing local LLM outputs against commercial NMTs (DeepL, Baidu), a frontier LLM (GPT-5.2), and professional-grade local NMT systems (OPUS-CAT, NeuralDesktop, Promt). Automatic evaluation is conducted with MATEO. Results reveal substantial variation in local LLM performance across language directions and model sizes. The best local LLMs match or surpass local NMT systems and a frontier LLM, though they remain behind top commercial NMTs. These findings underscore the viability of carefully selected local LLM translation for privacy-constrained professionals and inform future research on model scaling and multilingual capability.

Machine Translation (MT) has become a default language access tool in public and third sector organisations serving multilingual communities. However, how organisations, and their staff, actually use it, and their opinions about it, remain largely undocumented. This paper reports findings from charities, NGOs, community organisations and local government authorities in the West Midlands (UK), one of the most linguistically diverse areas of the country. The results indicate that MT use is widespread, informal, and driven by necessity rather than informed decisions or policies. Google Translate is the preferred tool; policies about MT use are rare, and confidence in translation quality is limited. Risk perception varies across the sector: local government respondents identify the widest range of concerns, including legal and medical, while third-sector organisations suggest a pragmatic approach. However, greater risk-awareness does not lead to greater governance, pointing to a gap between individual MT literacy and institutional accountability. Based on this, we propose some recommendations for how organisation serving multilingual communities should approach MT implementation and training.

This paper reports on a mixed-methods pilot study examining the potential of a large language model (LLM) for translating tourism promotional texts (TPTs), in comparison with a conventional neural machine translation (NMT) system, from English into Arabic. Four professional translators participated in a post-editing experiment followed by semi-structured interviews involving cue-based retrospection. The post-editing task aimed to provide empirical evidence of the effort involved in working with TPTs, while the interviews sought to capture participants’ judgments and subjective evaluations of the outputs. Overall, most participants exerted less effort post-editing LLM-generated outputs to a publishable standard compared to NMT outputs. They perceived the LLM outputs to be more creative, with creativity manifested through non-literal translations and aesthetic augmentation, while also noting that the outputs were unpredictable and far from perfect; in contrast, the NMT outputs were generally viewed as more informative yet lacking the promotional appeal needed for TPTs. The paper concludes by outlining the implications of the findings and suggesting several avenues for future research.

This study examines errors in a ChatGPT-mediated translation of a German economic text on inflation into Spanish, post-edited by 20 translation students. The analysis classifies 132 annotated instances by error origin (ChatGPT-generated versus student-introduced during post-editing) and by lin-guistic category. Results show that termi-nology is the highest-risk domain across the entire workflow (34.1%), followed by tense/aspect (15.2%) and style (13.6%). ChatGPT-related errors account for 50.8% of all instances, while student-introduced errors through over-editing represent 21.2%. A further 28.0% reflect acceptable alterna-tive reformulations. Students tend to trust fluent machine output even when it con-tains subtle semantic distortions, yet they also over-edit segments that are already ac-ceptable. The findings highlight three di-dactic priorities: developing LLM-based MT literacy, strengthening decision-making strategies in post-editing, and fostering gen-re- and domain-sensitive editing compe-tence. Implications for translator training and structured post-editing protocols are discussed.

As MT quality increases, interest in enhanced post-editing features such as QE-derived error highlights is growing, yet evidence for their usefulness remains limited. In this work, we explore the usefulness of LLM-derived error highlights and correction suggestions based on automatic post-editing (APE). We conduct a study where professional translators (En-Nl) post-edit translations using APE error highlights and correction suggestions. While no condition yielded productivity or quality gains compared to regular PE, APE highlights were better received than QE-derived highlights, and correction suggestions improved overall user experience.

This paper is the one of the first in-depth accounts of the use of machine translation (MT) by journalists. It reports on a study of Finnish journalists that was conducted mostly in 2024 and comprised an online survey with 68 responses and interviews with 10 journalists. Results revealed that participants fluently integrated MT into a variety of journalistic processes, with an emphasis on using it for assimilation and dissemination; that they relied largely on traditional online MT tools and tended to employ MT mostly with languages they have some competence in; and that they had some awareness of risk and strategies for mitigating it, but could benefit from guidelines and training on working with MT. The article contributes to research on the users and contexts for MT and, specifically, to its paraprofessional use in workplaces.

This article investigates the performance of automatic evaluation metrics (AEMs) and LLM-as-a-judge evaluation on literary translation across multiple languages, genres, and translation modalities. The aim is to assess how well these tools align with professionals when evaluating translation, creativity (creative shifts & errors), and see if they can substitute laborious manual annotations. A dataset of literary translations across three modalities (human translation, machine translation, and post-editing), three genres and three language pairs was created and annotated in detail for creativity by experienced professional literary translators. The results show that both AEMs and LLM-as-a-judge evaluations correlate poorly with professional evaluations on creativity, with LLM-as-a-judge showing a systematic bias in favour of machine-translated texts and penalising creative and culturally appropriate solutions. Moreover, performance is consistently worse for more literary genres such as poetry. This highlights fundamental limitations of current automatic evaluation tools for literary translation and the need to create new tools that do not frequently consider out of routine translations as errors.

This study examines the machine translation of audio descriptions (AD) as an alternative to producing new AD for audiovisual formats in a foreign language. To assess acceptance and comprehensibility among German users, a survey was conducted with blind and visually impaired participants, examining key AD strategies, such as character description and naming, facial expressions and gestures, and spatio-temporal settings. Participants compared machine-translated English AD with original German AD and provided feedback on these aspects. Results showed overall acceptance of the translated AD, although the original was generally preferred. Findings suggest that AD translation is feasible for the German audience, but further studies are needed on machine translation, production costs, as well as larger-scale user studies.

This paper presents findings from a survey on the use of automatic translation in Finnish public services, conducted in autumn 2025. Adapted from a similar survey conducted at the University of Bristol, the present survey focused on users whose main professional activity is not translation or interpreting. The study analyzed those professionals’ habits around automatic translation use, the purposes and the contexts involved, and the respondents’ satisfaction and confidence in using automatic translation. Approximately half of the respondents reported using automatic translation at least once a week and across a range of scenarios, including public-facing situations. While the survey sample is not representative of the Finnish public sector as a whole, the data suggests that automatic translation may play a role in the everyday work of at least some public service employees.

This work benchmarks safety across several large language models (LLMs) and compares their performances through red teaming, which simulates adversarial attacks and identifies vulnerabilities in the systems. Using two public datasets and a proprietary dataset, the models were tested with three purposes. First, a red teaming test was conducted to establish a safety comparison between five models in English and Portuguese. The results revealed that, in general, Sugarloaf 3.1 is the safest model, but that Vesuvius 4.0 slightly outperforms it in Portuguese, also revealing that both outperform GPT-4o. Afterwards, three models were tested with one guardrailing prompt, that encourages safe interactions, and two content moderation prompts, in both languages, to understand the strengths of the current guardrails, as well as the effectiveness of the content moderation task. The results show that current guardrails are sufficient, notwithstanding room for improvement (particularly for Portuguese), but that the performance of the content moderation task was substandard, even for the best performing model – GPT-4o. Finally, the 3.0 TowerLLM models were tested in English to evaluate the effect that tokens and temperature have on the output, revealing that an intermediate token limit leads to safer responses while a higher temperature causes performance degradation.

This paper investigates how post-editors of literary texts react and respond to the way metaphors have been translated by Neural Machine Translation (NMT) and Large Language Models (LLM). The results show that one in three metaphors in the output were changed by the posteditors, demonstrating that the translation of figurative language is indeed problematic in literary MT (LitMT). The responses indicate that the post-editors were aware of overly literal translations, though mostly for multiword expressions. Moreover, at times they found it difficult to determine whether solutions were acceptable. They rated the overall quality of the MT output as quite poor and stated that the post-editing was more work and more effort than it would have been translating from scratch. This supports previous studies arguing that post-editing constrains translators in their creativity and diminishes their sense of text ownership.

AI language technologies (AILTs), increasingly enabled by large language models (LLMs), are becoming embedded in multilingual healthcare workflows for translation, rewriting, documentation, interpreting, and messaging in language-discordant settings. Yet fluent output is not the same as clinically safe or equitable communication: performance varies across languages, accents, tasks, and workflows, and efficiency gains can hide errors, reduce traceability, and shift responsibility across clinicians, translators, interpreters, and health systems. This narrative review synthesises recent peer-reviewed evidence across written communication, spoken communication, and emerging agentic workflows. Using the Human-Centered AI Language Technology (HCAILT) lens, it examines capabilities, evaluation practices, implementation patterns, and recurrent errors through reliability, safety culture, and trustworthiness. We identify key convergences and contradictions in the literature and propose seven grand challenges for the next phase of research and deployment. Progress, we argue, requires not only better models but also accountable sociotechnical design, calibrated human oversight, and stronger collaboration across MT/NLP, translation studies, HCI, clinical practice, implementation science, and policy.

Cultural heritage institutions increasingly disseminate research and interpretive materials globally, but multilingual dissemination is constrained by limited budgets and staffing. In terminology-dense domains such as rock art, translation quality depends on accurate, consistent specialised terms, and small lexical errors can mislead non-specialists and reduce reuse. We compare three English MT setups for a Spanish academic rock art text: (1) DeepL as a strong NMT baseline, (2) Gemini-Simple (LLM with a basic prompt), and (3) Gemini-RAG (the same LLM with glossary-augmented prompting via lightweight retrieval of term pairs). Using PEARMUT, we conduct a human evaluation via (i) multi-way Direct Assessment (0–100) on 91 segments (1,743 Spanish words) and (ii) targeted terminology auditing with a restricted MQM taxonomy. Gemini-RAG yields the highest exact-match terminology accuracy (81.4%), versus Gemini-Simple (69.1%) and DeepL (64.4%), while preserving overall quality (mean DA 85.3 Gemini-RAG vs. 85.2 Gemini-Simple) and outperforming DeepL (80.3). These results show that glossary-augmented prompting is a low-overhead way to improve terminology control in cultural-heritage translation, provided that institutions maintain minimal terminology resources and lightweight evaluation procedures.

The aim of this paper is to propose a framework to support literary translators—particularly poetry translators—in making effective use of large language models (LLMs) through established prompt engineering strategies applied to both pre-translation and translation stages. The paper illustrates these strategies using poems characterized by multiple layers of syntactic, semantic, phonological, and cultural complexity, and discusses how LLMs perform in response to each prompting technique. It also engages with the longstanding claim that poetry translation is a purely human endeavour and cannot be computer-assisted, arguing instead that LLMs, rather than replacing human creativity, have the potential to extend it.

The integration of large language models (LLMs) into translation practice has substantially reshaped translation workflows (Kornacki and Pietrzak, 2025). Since translation quality depends partly on how these models are prompted, prompt design deserves closer attention as a key stage of the LLM-augmented translation process. This study investigates Spanish<>Italian translation with GPT 5.1 in the advertising and biomedical domains. It examines whether prompt language affects the quality of translations generated with the GPT 5.1 model, and specifically whether prompts written in the target language outperform prompts written in English, the language most prevalent in the model’s training data (Armengol-Estapé et al., 2022). Three prompt templates, varying in complexity and informational content, were tested. The translations were first screened for textual similarity, and only the translations generated from the template that produced the greatest variation across outputs were subsequently selected for human evaluation. Human judgments were collected through a pairwise-comparison task. The findings indicate that prompts written in the target language tend to yield higher-quality translations compared to prompts written in English.

This paper examines user reactions to the launch of the machine translation (MT) feature on Xiaohongshu, a Chinese social media and e-commerce platform, in January 2025. Drawing on a dataset of 6,723 comments collected from 11 official posts promoting the translation function, this paper combines sentiment analysis with thematic analysis to investigate how users perceived and experimented with this function. Results show that reactions were generally positive, although concerns about functionality, accessibility, and translation accuracy were also expressed. In addition, users actively tested the function with inputs that fail to represent everyday online communication, including stand-alone words and phrases, abbreviations, internet slang, and symbolic or encoded forms. Successful decoding of these texts elicited positive responses, while testing of more conventional language remained fairly limited. This could lead to uncritical acceptance of MT outputs by users, highlighting the importance of closer collaboration among computer scientists, translation scholars, and platform designers to improve MT performance and promote informed user engagement in real-world deployment of MT functions.

The present paper focuses on the analysis of automatic subtitles produced with three different systems. We compare the outputs among each other paying attention to the categories of quality derived from audio-visual translation quality research. Besides that, we also consider comprehensibility of the produced subtitles. Additionally, we analyse the automatic evaluation scores to assess the overall quality. Our results show that automatically generated subtitles subtitles remain below human standards in quality and comprehensibility.

AI-based technologies have disrupted the translation profession. While surveys such as the annual European Language Industry Survey have provided regular reports on the situation in Europe, less is known about the effects of AI-based translation tools on professional translators working elsewhere. This study reports on a survey that was conducted with support from the professional translators association of Quebec in Canada (Ordre des traducteurs, terminologues et interprètes agréés du Québec). One hundred and seventy-five completed surveys, along with additional partial responses, were analyzed. This article reports on questions relating to two broad categories: general perceptions about AI’s influence on the translation profession, and the evolution and sustainability of the profession. Where relevant, the results of this survey are compared to those from other parts of the world. Findings show that while Quebec translators face many issues that are similar to those faced by translators in regions such as the United Kingdom, France, Belgium, Switzerland, and Europe more generally, there are subtle differences also, such as the tendency of many Quebec translators to work as generalists, the comparatively low number of Quebec translators working in the Entertainment, Arts and Culture domains (which are showing signs of growth elsewhere), and the large number who seem hesitant to supervise interns moving forward.

Specialised translation relies on the use of documentary and terminological resources, including corpora. These resources are particularly useful for terminology. However, their compilation and exploitation have several limitations: they require time, technical skills and access to data that can be difficult to collect. This study examines the extent to which LLMs can assist specialised translators in finding equivalents from English to French. We evaluate four proprietary models, GPT-4o, GPT-5.2, Claude Sonnet 4.5 and DeepSeek, in two specialised domains, Earth, Environmental and Planetary Sciences (EEPS) and Natural Language Processing (NLP). The experiment is based on 80 terms per domain and compares two prompting strategies: a terminology and a translation mode. The results highlight clear differences between models, prompting strategies and, to a lesser extent, domains. Claude Sonnet 4.5 achieves the best results in the most favourable configuration, while DeepSeek stands out for its greater stability. Analysis of confidence estimates also shows that they are only a partial indicator of terminological accuracy. Overall, the findings suggest that LLMs can be useful tools for specialised translators, but cannot, at this stage, replace specialised corpora. This research therefore paves the way for future work on the real practical usefulness of LLMs for specialised translators in work and educational contexts.

This study explores the influence of two prompting strategies on the lexical and syntactic metrics of the LLM-powered machine translations (MTs) of a corpus of 18~British editorials into French as well as their impact on the edit types made by Master’s translation students post-editing from a representative editorial of the corpus, as evaluated using the MTPEAS taxonomy. Quantitatively, the prompt specifically requesting more syntactic and lexical variety leads to significantly higher syntactic and lexical metrics scores in the MTs, but differences remain significant only for lexical metrics in the post-edited versions of the representative editorial. Qualitatively, we show that students post-editing from an MT featuring more idiomatic rephrasings and fewer syntactic calques (as opposed to an MT that is structurally closer to the source text) seem to make fewer edits overall, leave more MT errors unaddressed, and make fewer successful edits.

This study examines the influence of prompt language and translation theory-driven prompt design on the quality of Spanish–Chinese editorial translations generated by GPT-5.2. A parallel corpus of four EL PAÍS editorials was translated under 48 experimental conditions (4 prompt types × 3 prompt languages × 4 articles). Translation quality was assessed using BLEU and BERTScore-F1 for automated evaluation, alongside human evaluation based on the Multidimensional Quality Metrics (MQM) framework. Automated metrics identified the baseline prompt (BASE) as the best-performing condition, whereas human evaluation ranked the brief-oriented prompt (BRIEF) highest (MQM: 8.66 vs. 7.84), a reversal attributed to the single-reference constraint inherent in automated measures. Subtype analysis indicated that translation theory-driven prompts selectively reduced Awkward style errors, whereas Unidiomatic style errors persisted consistently across conditions. Prompt language exhibited negligible impact under both evaluation paradigms. These results indicate that translation theory-driven prompts are advantageous for language learners seeking high-quality editorial translations and underscore the necessity of human evaluation for accurately assessing LLM translation quality in this domain.

he Erasmus+-funded international research consortium LT-LiDER develops a range of digital training resources which are grounded in the overarching frameworks of digital and AI literacy and oriented towards practical application contexts in the language and translation industry. These resources can be implemented on a component basis or as a complete curriculum in higher-education language and translation classrooms.

This paper reports on the final stages of the MaTIAS project. A functional prototype of the multilingual notification tool was deployed across seven Belgian reception centres, accompanied by training and technical support. Feedback was gathered through interviews and surveys. Two rounds of machine translation evaluation revealed considerable differences in quality across languages. The translation quality of Tigrinya in particular was deemed too low to be usable.

The Anglocentric nature of scholarly communication has many implications, such as limiting publication, discoverability and access from other language communities (even for major languages); putting minoritized languages at risk in the academic domain; and excluding many from peer review. The OSCAIL project addresses these challenges by exploring how machine translation (MT) enhanced by large language model (LLM)–based technologies can support access to scientific knowledge. Outputs will include evaluation datasets, protocols and best practices for MT in scholarly communication, and a prototype integration of MT tools into Open Journal Systems, the world’s most widely used open-source scholarly publishing platform.

We present AIDA Agents, a multi-agent translation platform that orchestrates LLM-based agents—for translation, rating, post-editing, and re-rating—delivering context-aware translations without model fine-tuning. Optional retrieval-augmented generation (RAG) injects translation memories, terminology, and style guidelines at every pipeline stage. On WMT24++ (Deutsch et al., 2025) (11 languages), AIDA Agents outperforms all systems on 10 of 11 pairs. On an industrial benchmark, 70–98% of segments are publication-ready without human post-editing. The platform is deployed with native XLIFF integration.

We present VERA, an easy-to-use platform for machine translation (MT) evaluation that integrates automatic and human evaluation within a single web environment. VERA supports standard reference based metrics and multiuser annotation following the Multidimensional Quality Metrics (MQM) Core framework. The platform enables the export of annotated corpora and the generation of final PDF reports summarizing both automatic and human evaluation results, including correlations between them.

This document presents an initial overview of the CLingS project, currently in its early development stage. It outlines a collaborative effort to build a cross-lingual information retrieval platform for scientific literature in underrepresented languages. The project CLingS aims to develop datasets, tools, and methods to improve multilingual access to scientific knowledge.

The ARTICULATE project is an ambitious and interdisciplinary initiative funded by the CHIST-ERA call 2025. Its vision is to revolutionize science education and democratize scientific knowledge beyond academia and English-speaking audiences through the integration of AI with self-regulated learning. The aim is to translate science not just across language but across language style, to create engaging spoken digital experiences. We present an introduction to this project, an overview of the consortium and research approach, and a number of expected impacts.

Human evaluation remains essential for reliable machine translation (MT) assessment, yet practical evaluation workflows are often difficult to reproduce and scale. Here we introduce HERMeS, a lightweight human evaluation platform designed to streamline systematic human evaluation and comparison of multiple MT systems across large translation sets. Unlike existing evaluation tools, HERMeS focuses specifically on scalable comparison of many anonymized systems through a hybrid ranking and direct assessment workflow, using a novel approach that reduces evaluator cognitive load while maintaining data quality, security, and integrity.

Domain adaptation remains a major challenge for machine translation, par-ticularly in institutional communica-tion. This paper presents the MULTI-TRAD project , which develops English–Spanish parallel corpora for the Third Social Sector communication. The project integrates three comple-mentary objectives: (i) the compilation of a domain-specific parallel corpus, (ii) the analysis of linguistic variation across human translation (HT), ma-chine translation (MT), and post-edited (PE) texts using Multidimensional Analysis (Biber, 1988), and (iii) the development of a domain-adapted neu-ral machine translation system. In par-ticular, the project investigates how dif-ferent translation processes give rise to distinct functional profiles, related to phenomena such as translationese and post-editese. This paper presents the project design and initial progress.

This paper presents PCDT, a web-based platform for collecting sentence-aligned parallel corpora through a community-driven approach to support machine translation for under-resourced languages. The tool decentralizes the translation task to the target community and subsequently reviewed by language experts.

This article describes the research project aimed at developing a Trilingual Machine translation System for English, Nepali, and Tamang language pairs. This project is expected to address knowledge and communication gaps caused by language barriers and mitigate disparities in the availability of information and knowledge sources in Tamang and Nepali.

The TELÓ project provides an open-source framework for automated subtitling in the performing arts. Integrating state-of-the-art ASR and NMT, the system enables bidirectional translation between Catalan, Spanish, English and French. Designed for live performances, it provides synchronized captions for multiple devices, enhancing cultural internationalization and accessibility.

DA + Criteria is a translation quality assessment method proposed based on a comprehensive systematic literature review on the concepts of quality in machine translation and translation studies. In the presented project the method was tested alongside MQM on the German translations by humans, DeepL and ChatGPT of English non-fiction texts, using the results of the study as well as the participants’ answers to further refine the method.

We present ACATMT, a compact bilingual encoder-decoder NMT system for English and Swedish, designed for professional computer-assisted translation (CAT) tools. It runs on-device in ONNX format, under 1 GB of RAM with no GPU needed, and features real-time post-edit based terminology adaptation. It also supports translation memory conditioning via decoder prefilling. Evaluation on 5,021 technical segments unseen during training shows significant improvements in COMET and BLEU when using glossaries.

Video-based sign language dictionary search – in which a user records a sign to retrieve its translation – has been increasingly studied, yet never deployed in a large-vocabulary setting. We present the first such deployment: a fully scalable video-based search system integrated into the Flemish Sign Language (VGT) Dictionary, comprising over 11,000 signs. The system, released on November 28th, 2025, requires no retraining as new signs are added, and was validated on data collected in the wild. It was developed through an equal partnership between the deaf-led Flemish Sign Language Centre (VGTC) and AI researchers from Ghent University, and shows that closing the gap between sign language research and community impact is both achievable and essential.

This paper presents the TaMTAS project (Terminology-Aware Machine Translation for Accessible Science), a research project coordinated by the Universitat Oberta de Catalunya (UOC) to develop an open- source translation ecosystem for the Life Sciences. While we provide a general overview of the project’s organization into seven Work Packages (WPs) and its col- laborative consortium, this article focuses specifically on the work of WP2. Led by the UOC, this package is responsible for the parallel corpus compilation for five lan- guages (English, Spanish, Catalan, Esto- nian, and Irish), the enhancement of TBX- Tools for terminology extraction, and the development of synthetic data augmenta- tion strategies. These linguistic assets are essential to power the downstream Large Reasoning Models (LRMs) and Automatic Post-Editing (APE) modules, ensuring ter- minological consistency in highly special- ized scientific domains.

Recently, generative artificial intelligence (GenAI) has been perceived as a "silver bullet" for achieving faster, cheaper, and better translation production. However, in professional localisation, AI capabilities alone are not enough, as the still time-consuming post-editing (PE) of machine translation (MT) and GenAI output proves. The features and processes presented in this work aim to reduce these ef-forts by enhancing terminological control and translation consistency within the CAT environment STAR Transit.

Translation 2.0 addresses a critical gap in accessible, up-to-date educational resources on recent developments in Machine Translation and Large Language Models for students of linguistics and translation. It develops an online module with open-access learning materials, including knowledge clips, a workbook with incremental exercises to consolidate conceptual understanding, practical coding guides, and industry professional videos. The module aims to build both subject knowledge and computational literacy, freeing up contact hours for deeper engagement and critical discussions on practical, professional and ethical aspects. Translation 2.0 is funded through an Educational Innovation grant by the Faculty of Humanities at Leiden University and ECOLe (Expert Centre for Education and Learning) and runs from February to December 2026.

Access to the primary labor market for people with cognitive impairments is hampered by barriers, notably the lack of workplace information in Easy Language (EL). Producing such texts is time- and cost-intensive and requires specialized translators. The project STARK-LS (Strengthening participation in the primary labor market through AI-generated Easy Language) addresses this gap by implementing an AI-translation tool to translate workplace materials into EL and embedding the approach in internships for people with cognitive impairments. An interdisciplinary project team conducts mixed-methods evaluations by testing the EL translations for applicability, comprehensibility, and acceptance using lab-based eye-tracking and questionnaire studies, qualitative interviews with interns with cognitive impairments and experts for EL, and a quantitative online survey with company representatives. The findings will lead to process models and best-practice recommendations for companies and rehabilitation agencies. The project advances scientific understanding of the perceived usefulness and potential barriers of EL in organizational contexts, while critically evaluating AI’s influence on the diffusion of high-quality EL texts in companies. Funded by the German Federal Ministry of Labour and Social Affairs, the project aims to scale high-quality accessible communication and promote sustainable inclusion in the German labor market.

Prompsit Language Engineering is launching an updated API and CLI for its open-source, planet-friendly machine translation services. Operating on a freemium model, the tools offer free limited access alongside tiered pricing for advanced features like MT evaluation, quality estimation, corpus scoring, and multilingual dataset annotation.

This paper presents the Multilingual, Multicultural, and Multimodal Medical Language Processing (4MLP) Project, funded through a competitive call of the University of Naples ”L’Orientale” (Italy). 4MLP aims at developing multilingual,multicultural, and multimodal language technologies for healthcare to bridge complex medical knowledge and patients’ needs, supporting inclusive and effective healthcare communication while advancing explainable and culturally aware Artificial Intelligence.

This paper introduces Ouvia, a research project to assess user-perceived usability and reliability of modern speech translation tools in En→Pt scenarios. The project centers on a user study in which we simulate real-life daily interactions by recruiting crowdworkers online from different sociodemographic groups. We collect their spoken requests and self-assessments about quality, satisfaction, and reliability. Here, we describe the project’s motivation and objectives, the study design, and the expected outcomes we will provide to speech translation practitioners.

The CRITICS project addresses science accessibility and literacy through the convergence of advanced Machine Translation (MT) based on Large Language Models (LLMs) and educational technology. By leveraging MT systems specifically optimized for scientific content, educational institutions can provide accurate, culturally relevant translations of scientific materials in higher-education students’ native languages, ensuring that complex scientific concepts are comprehensible while maintaining technical accuracy. Novel research on MT specifically tailored for scientific documents aims to break down language barriers in accessing cutting-edge research and educational materials currently only available in high-resourced languages such as English, thereby facilitating the democratization of scientific knowledge across linguistic boundaries.

This study evaluates an AI post-editing (AIPE) system in a professional translation setting, covering translation from English into ten target languages across five domains. We evaluate the system using automatic metrics on 71,262 production segments and human evaluation on a stratified sample of 6,618 segments (approximately 600 segments per target language) assessed by 60 professional translators. AIPE refines machine translation output using a secure publicly available LLM, retrieving language-specific style guides and high-quality bilingual examples to guide edits. We compare it with direct LLM translation (LLMT), Google Translate, and DeepL. The two AIPE configurations evaluated consistently outperform the generic translation baselines in terms of quality. LLMT does not match this quality, though it may suit less quality-sensitive domains. We observe how AIPE’s gains vary according to pre-translation type, with fuzzy translation memory matches over-represented among severe errors, and discuss deployment implications.

We investigate whether reasoning information can enhance machine translation when incorporated as supportive context during training and inference. Using Hindi-Bengali translation as a case study, we define five reasoning components: Key Terms, Syntactic, Semantic, Pragmatic, and Paraphrase. We conduct a complete ablation across all 31 possible combinations using Gemma-3-1B-Instruct and evaluate on multi-domain benchmark with BLEU, chrF, and TER. Evaluation results show that reasoning effectiveness depends on its type and composition rather than quantity. Combining multiple heterogeneous signals causes objective diffusion, degrading performance. The compact Semantic and Paraphrase combination proves optimal, and providing it during inference yields 23.86 BLEU compared to 22.12 from standard fine-tuning a +1.74 BLEU gain across eight domains. These findings demonstrate that targeted semantic guidance consistently and meaningfully improves the compact translation models.

Machine translation quality estimation (QE) typically relies on dedicated neural models trained on human judgments. We evaluate whether cosine similarity over general-purpose embeddings can serve as a lightweight alternative, using Gemini embeddings as the scoring backbone. Through three experiments (rogue dimension analysis, score calibration, and a learned calibration head) and a root cause analysis, we find that cosine similarity between source and translation saturates in the 0.94–0.99 range because even poor translations preserve most of the source semantics, leaving an Area Under the ROC Curve (AUC) ceiling of approximately 0.63. However, a LightGBM classifier trained on normalized cosine and surface-level text features breaks through this ceiling (AUC 0.751), with the improvement driven primarily by features orthogonal to embedding similarity.

Valencian, the Western Catalan variety used in the Valencian Community of Spain, lacks a dedicated language code in most multilingual machine translation (MT) systems, and is systematically rendered closer to the standard written Eastern Catalan used in Catalonia. We address this gap by adapting TranslateGemma-4B-IT, a 4-billion-parameter instruction-tuned (IT) large language model (LLM) specialized for translation, via three post-training strategies using only public corpora and Quantized Low-Rank Adaptation (QLoRA): (i) supervised fine-tuning (SFT); (ii) Group Relative Policy Optimization (GRPO), a reinforcement learning (RL) technique, with chrF plus a naturalness reward (GRPOV1); and (iii) GRPO with a composite automatic-metric reward (GRPOV2). Our results suggest that reward-function alignment with the target dialect is a key determinant of RL success in low-resource dialectal MT.

Movie subtitle translation is inherently multimodal, yet text-only systems often miss visual cues needed to convey emotion, action, and social nuance, especially for low-resource Indic languages (English to Hindi, Bengali, Telugu, Tamil and Kannada). We present a case study on five full-length films and compare two lightweight visual grounding strategies: structured attribute summaries from a 5-minute sliding window and free-text summaries of inter-subtitle visual gaps. Our analysis shows that temporal misalignment between subtitles and frames is a major obstacle in long-form video, often rendering indiscriminate visual grounding ineffective. However, oracle selective grounding, which replaces only the lowest-quality 20-30

Vision-language models (VLMs) have the potential to enhance machine translation (MT) by leveraging visual context alongside text, yet their real utility for production workflows remains unclear. We conduct a unified, multi-condition evaluation of six leading VLMs—both open and proprietary—on two challenging benchmarks (CoMMuTE and CaMMT), targeting lexical and cultural disambiguation respectively, with a domain-style case study simulating technical documentation localization. Results show that model performance varies widely, and the benefit of relevant images does not necessarily transfer across use cases. Proprietary models are notably sensitive to irrelevant images while open-source models are generally more stable; incorrect or contradicting visuals, by contrast, degrade translation across all models. Taken together, these findings make rigorous evaluation a necessary precondition for production deployment: metric gains can mask real accuracy losses in technical domains, model sensitivity to irrelevant images should inform model selection, and reliable image–text matching is a hard requirement for any pipeline.

English dominates scientific publishing, which disadvantages researchers who are not native English speakers, especially those in the earlier stages of their careers. Being able to write and engage with scientific content written in their own language would clearly facilitate scientific production. The MaTOS project (Machine Translation for Open Science) seeks to reduce these barriers by developing machine translation tools for scientific documents in English and French. This article presents the design of the MaTOS pipeline for the HAL platform to automatically translate article abstracts, with author validation, to increase the number of bilingual abstracts on the platform. We also report preliminary experiments comparing translation of sentence, three-sentence chunks, and whole abstracts, evaluated using quality estimation metrics.

Style guides are a centrepiece of professional translation workflows. Yet, their integration into automatic pipelines remains underexplored. This paper presents exploratory work on information extraction from client style guides and application to a templated style guide, developed to be a system prompt. This template is then applied during an LLM-based translation to automatically produce outputs that are compliant to client’s requirements. The study focused on seven language pairs~(LP), evaluating the automatic extraction, and translation quality and compliance with the style guide. The extraction demonstrated reliable performance across languages and file formats. Translation quality and adherence were evaluated using human preference annotation, comparing two Tower models (Tower Zen 9B and Tower+ 72B). The results indicate a modest advantage for Tower+, but with mutual acceptability in certain instances. These findings establish a viable semi-automatic framework for style guide integration in translation workflows, and motivate further investigation across broader domains, clients, and LPs.

Since 2023, translators for the Parliament of Canada have had the option to use neural machine translation (NMT) technology provided by the National Research Council of Canada (NRC) to support their work in translating parliamentary publications between French and English. We present our analysis of an anonymized dataset of translators’ interactions with our Hawkeye MT systems, collected since their introduction and covering a period of 2.5 years. This data provides a unique perspective on how translators interact with the systems, how their use evolved over time and how it impacts the nature of their translations.