Multimedia localization workflows are inherently complex, and the demand for localized content continues to grow. This demand has attracted Language Service Providers (LSPs) to expand their activities into multimedia localization, offering subtitling and voice-over services. While a wide array of AI tools is available for these tasks, their value in increasing productivity in multimedia workflows for LSPs remains uncertain. This study evaluates the productivity, quality, cost, and time efficiency of three multimedia localization workflows, each incorporating varying levels of AI automation. Our findings indicate that workflows merely replacing human vendors with AI tools may result in quality degradation without justifying the productivity gains. In contrast, integrated workflows using specialized tools enhance productivity while maintaining quality, despite requiring additional training and adjustments to established practices.

Despite the remarkable development of artificial intelligence (AI) and machine translation (MT) in recent years, which has made them more efficient, less costly and easier to navigate, they still struggle to match the abilities of human translators. The limitations shown by AI and MT, which have been detected in various domain-specific texts and contexts, sustain the debate over whether they can fully replace human translators. Nevertheless, very few studies have examined the translation abilities of AI and MT during conflicts and high-stakes contexts. This paper explores some of these limitations that were detected during the 2023 Gaza-Israel conflict, illustrating significant examples from X (formerly Twitter). These examples showcase limitations in 1) translating cultural references, 2) avoiding critical errors in high-stakes context, 3) preventing bias and intervention, and 4) translating cursive handwriting. This is done through a combination of descriptive, comparative and experimental analysis methods, highlighting risks and implications associated with using these tools in such sensitive contexts, while contributing to the broader discussion on whether advances in AI and MT will diminish the need for human translators.

The popularity of automatic speech-to-speech translation for human conversations is growing, but the quality varies significantly depending on the language pair. In a context of community interpreting for low-resource languages, namely Turkish and Pashto to/from French, we collected fine-tuning and testing data, and compared systems using several automatic metrics (BLEU, COMET, and BLASER) and human assessments. The pipelines consist of automatic speech recognition, machine translation, and speech synthesis, with local models and cloud-based commercial ones. Some components have been fine-tuned on our data. We evaluated over 60 pipelines and determined the best one for each direction. We also found that the ranks of components are generally independent of the rest of the pipeline.

In an era of rapid technological advancements, communication continues to evolve as new linguistic phenomena emerge. Among these is Arabizi, a hybrid form of Arabic that incorporates Latin characters and numbers to represent the spoken dialects of Arab communities. Arabizi is Widely used on social media and allows people to communicate in an informal and dynamic way, but it poses significant challenges for machine translation due to its lack of formal structure and deeply embedded cultural nuances. This case study is motivated by a growing need to translate Arabizi for gisting purpose. It evaluates the capacity of different LLMs’ to decode and translate Arabizi, focusing on multiple Arabic dialects that have rarely been studied up until now. Using a combination of human evaluators and automatic metrics, this research project investigates the model’s performance in translating Arabizi into both Modern Standard Arabic and English. Key questions explored include which dialects are translated most effectively and whether translations into English surpass those into Arabic.

This research explores Cultural Transcreation (CT) for East Asian languages, focusing primarily on Mandarin Chinese (ZH) and the customer service (CS) market. We combined Large Language Models (LLMs) with prompt engineering to develop a CT product that, aligned with the Augmented Translation concept, enhances multilingual CS communication, enables professionals to engage with their target audience effortlessly, and improves overall service quality. Through a series of preparatory steps, including guideline establishment, benchmark validation, iterative prompt refinement, and LLM testing, we integrated the CT product into the CS platform, assessed its performance, and refined prompts based on a pilot feedback. The results highlight its success in empowering agents, regardless of linguistic or cultural expertise, to bridge effective communication gaps through AI-assisted cultural rephrasing, thus achieving its market launch. Beyond CS, the study extends the concept of transcreation and prompt-based LLM applications to other fields, discussing its performance in the language conversion of website content and advertising.

As strong machine translation (MT) systems are increasingly based on large language models (LLMs), reliable quality benchmarking requires methods that capture their ability to leverage extended context. This study compares two commercial MT systems – DeepL and Supertext – by assessing their performance on unsegmented texts. We evaluate translation quality across four language directions with professional translators assessing segments with full document-level context. While segment-level assessments indicate no strong preference between the systems in most cases, document-level analysis reveals a preference for Supertext in three out of four language directions, suggesting superior consistency across longer texts. We advocate for more context-sensitive evaluation methodologies to ensure that MT quality assessments reflect real-world usability. We release all evaluation data and scripts for further analysis and reproduction at https://github.com/supertext/evaluation_deepl_supertext.

Localization strategies can differ widely between languages, but the necessity and efficiency of maintaining distinct strategies for closely related variants of the same language is debatable. This paper explores the potential for unifying localization strategies across different Spanish locales, leveraging Large Language Models, prompting techniques, and specialized linguistic resources to perform cross-locale adaptations from a chosen baseline. In this study, we examine and develop vocabulary, terminology, grammar, and style transformation methods from Latin American into Mexican and Argentine Spanish. Our findings suggest that parting from a core translation and then following an automated adaptation process to unify localization strategies is feasible for Spanish diverse variants, regardless of the type of divergence each of them has from the baseline locale. However, even if the need for human post-editing is then minimal compared to a fully ‘manual’ cross-locale adaptation, the linguistic review remains crucial, particularly for editing style nuances.

This work presents the details and findings of the first mentorship in speech translation (SpeechT), which took place in December 2024 and January 2025. To fulfil the mentorship requirements, the participants engaged in key activities, including data preparation, modelling, and advanced research. The participants explored data augmentation techniques and compared end-to-end and cascaded speech translation systems. The projects covered various languages other than English, including Arabic, Bengali, Galician, Indonesian, Japanese, and Spanish.

Recent years have witnessed the development of advanced language technologies, including the use of audio and images as part of multimodal systems. However, these models are not adapted to the specific needs of migrants and Non-Governmental Organizations (NGOs) communicating in multilingual scenarios. In this project, we focus on the situation of migrants arriving in the Basque Country, nearby the western border between Spain and France. For identifying migrants’ needs, we have met with several organisations helping them in different stages, including: sea rescue; primary care in refugee camps and in situ; assistance with asylum demands; other administrative issues; and human rights defence in retention centres. In these interviews, Darija has been identified as the most spoken language among the under-served ones. Considering this, we have started the development of a Machine Translation (MT) system between Basque and Darija (Moroccan Arabic), based on open-source corpora. In this paper, we present the description of the project and the main results of the participatory research developed in the initial stage.

We present key interim findings from the ongoing MaTIAS project, which focuses on developing a multilingual notification system for asylum reception centres in Belgium. This system integrates machine translation (MT) to enable staff to provide practical information to residents in their native language, thus fostering more effective communication. Our discussion focuses on three key aspects: the development of the multilingual messaging platform, the types of messages the system is designed to handle, and the evaluation of potential MT systems for integration.

This paper introduces CAT-GPT, an innovative Computer-Assisted Translation (CAT) tool designed to address context-awareness and terminological consistency challenges often encountered in standard CAT workflows. Grounded in Skopos theory (Vermeer, 2014) and powered by a Large Language Model (LLM) backend, CAT-GPT integrates context-sensitive segmentation, automatically generated and adjustable translation instructions, and an advanced machine translation component. Comparative observations with a widely used CAT tool (e.g., Trados Studio) suggest that CAT-GPT reduces post-editing effort and improves text-level coherence, especially in specialized or domain-specific scenarios.

In this paper, we present the latest version of MTUOC-server and MTUOC-multiserver, a robust tool capable of launching one or more translation servers. It supports a wide range of NMT systems and LLM models, both commercial and open-source, and is compatible with several communication protocols, broadening the range of tools it can work with. This server is a component of the MTUOC project and is distributed under an free license.

This paper discusses the capabilities and benefits of OPAL Enable, an advanced AI suite designed to modernize localization processes. The suite comprises Machine Translation, AI Post-Editing, and AI Quality Estimation tools, integrated into renowned translation management systems. The paper provides an in-depth analysis of these features, detailing their procedural order, and the time and cost savings they offer. It emphasizes the customization potential of OPAL Enable to meet client-specific requirements, increase scalability, and expedite workflows.

UniOr PET is a browser-based platform for machine translation post-editing and a modern successor to the original PET tool. It features a user-friendly interface that records detailed editing actions, including time spent, additions, and deletions. Fully compatible with PET, UniOr PET introduces two advanced timers for more precise tracking of editing time and computes widely used metrics such as hTER, BLEU, and ChrF, providing comprehensive insights into translation quality and post-editing productivity. Designed with translators and researchers in mind, UniOr PET combines the strengths of its predecessor with enhanced functionality for efficient and user-friendly post-editing projects.

A significant percentage of the population of Guatemala and Mexico belongs to various Mayan indigenous communities, for whom language barriers lead to social, economic, and digital exclusion. The Mayan languages spoken by these communities remain severely underrepresented in terms of digital resources, which prevents them from leveraging the latest advances in artificial intelligence. This project addresses that problem by means of: 1) the digitisation and release of multiple printed linguistic resources; 2) the development of a high-quality parallel machine translation (MT) evaluation corpus for six Mayan languages. In doing so, we are paving the way for the development of MT systems that will facilitate the access for Mayan speakers to essential services such as healthcare or legal aid. The resources are produced with the essential participation of indigenous communities, whereby native speakers provide the necessary translation services, QA, and linguistic expertise. The project is funded by the Google Academic Research Awards and carried out in collaboration with the Proyecto Lingüístico Francisco Marroquín Foundation in Guatemala.

Neural Machine Translation intensifies educational challenges in translation technologies. The MultiTraiNMT project developed MutNMT, an open-source, didactic platform for training and evaluating NMT systems. Building upon it, LT-LiDER introduces ProMut which implements three main novel features: migration of the core NMT framework from JoeyNMT to MarianNMT, close integration with OPUS datasets, engines and connectors and the addition of a researcher profile for larger datasets and extended training processes and evaluation.

This paper presents an updated overview of the ‘BridgeAI’ project, a science-for-policy initiative funded by the Portuguese Foundation for Science and Technology (FCT) and the Recovery and Resilience Programme. In its second stage of implementation, BridgeAI continues to build upon its original goals, working towards a strategy to align AI research, policy, regulatory frameworks, and practical application. The project provides Portugal with an evidence-based framework to implement the EU Artificial Intelligence (AI) Act (AIA), ensuring responsible AI innovation through multidisciplinary collaboration. BridgeAI connects academia, industry, public administration, and civil society to create actionable insights and regulatory recommendations. This paper details the project’s latest advancements, key recommendations, and future directions.

The objective of the DeMINT project is to develop a conversational tutoring system aimed at enhancing non-native English speakers’ language skills through post-meeting analysis of the transcriptions of video conferences in which they have participated. This paper describes the model developed and the results obtained through a human evaluation conducted with learners of English as a second language.

The “App for post-editing neural machine translation using gamification” (GAMETRAPP) project (TED2021-129789B-I00), funded by the Spanish Ministry of Science and Innovation (2022–2025) and led by the University of Málaga, has been in progress for two and a half years. The project is developing a web application that incorporates a gamified environment, specifically a virtual escape room, to bring post-editing practice closer to scholars. This paper outlines the methodological process followed and provides a brief description of the virtual escape room.

The AI4Culture project, funded by the European Commission (2023-2025), developed a platform (https://ai4culture.eu) to educate cultural heritage (CH) professionals in AI technologies. Acting as an online capacity building hub, the platform describes openly labeled data sets and deployable and reusable tools applying AI technologies in tasks relevant to the CH sector. It also offers tutorials for tools and recipes for the combination of tools. In addition, the platform allows users to contribute their own resources. The resources described by project partners involve applications for optical or handwritten character recognition (OCR, HTR), generation and validation of subtitles, machine translation, image analysis, and semantic linking. The partners customized various tools to enhance the usability of interfaces and components. Here, we zoom in on the use case of correcting OCR/HTR output using various means (such as an unstructured manual transcription) to facilitate multilingual accessibility and create structured ground truth (text lines with image coordinates).

We describe the progress of the High Performance Language Technologies (HPLT) project, a 3-year EU-funded project that started in September 2022. We focus on the up-to-date results on the release of free text datasets derived from web crawls, one of the central objectives of the project. The second release used a revised processing pipeline, and an enlarged set of input crawls. From 4.5 petabytes of web crawls we extracted 7.6T tokens of monolingual text in 193 languages, plus 380 million parallel sentences in 51 language pairs. We also release MultiHPLT, a cross-combination of the parallel data, which produces 1,275 pairs, as well as releasing the containing documents for all parallel sentences in order to enable research in document-level MT. We report changes in the pipeline, analysis and evaluation results for the second parallel data release based on machine translation systems. All datasets are released under a permissive CC0 licence.

This paper is a short presentation of MaTOS, a project focusing on the automatic translation of scholarly documents. Its main aims are threefold: (a) to develop resources (term lists and corpora) for high-quality machine translation; (b) to study methods for translating complete, structured documents in a cohesive and consistent manner; (c) to propose novel metrics to evaluate machine translation in technical domains. Publications and resources are available on the project web site: https://anr-matos.gihub.io.

The aim of this project was to curate data for the English-Malayalam language pair for the tasks of Quality Estimation (QE) and Automatic Post-Editing (APE) of Machine Translation. Whilst the primary aim of the project was to create a dataset for a low-resource language pair, we plan to use this dataset to investigate different zero-shot and few-shot prompting strategies including chain-of-thought, towards a unified explainable QE-APE framework.

MTxGames is a doctoral research project examining three different translation modes with varying degrees of machine translation post-editing when translating video game texts. For realistic experimental conditions, data elicitation took place at the workplaces of professional game translators. In a mixed-methods approach, quantitative data was elicited through keylogging, eye-tracking, error annotation, and questionnaires as well as qualitative data through interviews. Aspects to be analyzed are translation productivity, cognitive effort, translation quality, and translators’ user experience.

The DECA project consortium investigates epistemic capacities, defined as an individual’s access to reliable knowledge, their ability to participate in knowledge production, and society’s capacity to make informed, sustainable policy decisions. As a tool both for accessing information across language barriers and for producing multilingual information, machine translation also plays a potential role in supporting these epistemic capacities. In this paper, we present an overview of DECA’s research on two perspectives: 1) how migrants use machine translation to access information, and 2) how journalists use machine translation in their work.

We present Reverso Define, an innovative English dictionary designed to support translation professionals with AI-powered, context-aware definitions. Built using a hybrid approach combining Large Language Models and expert linguists, it offers precise definitions with special attention to multi-word expressions and domain-specific terminology. The system provides comprehensive coverage of technical domains relevant to professional translators while maintaining daily updates to address emerging terminology needs. It also provides indicative translations in 26 languages linked to each meaning, and variants within languages, when appropriate, and has links to Reverso Context, the range of contextual and corpus-based bilingual dictionaries, and Reverso Synonyms. We will show the various ways to use it with concrete examples and give some insights on its design and creation process.

Reverso Documents is a widely-adopted translation and post-editing platform that combines advanced machine translation with extensive document format support and layout preservation capabilities. The system features AI-based rephrasing, bilingual dictionaries, and translation memory integration, enabling both professional translators and general users to work efficiently with complex documents. Used by millions globally, it provides API access for workflow integration and batch processing. The upcoming 2025 release will introduce LLM-based translation with customizable settings, allowing for enhanced control over translation outputs while maintaining document structure and translation quality.

Minority languages such as Irish are massively under-resourced, particularly in terms of high-quality domain-relevant data, limiting the capabilities of machine translation (MT) engines, even those integrating large language models (LLMs). The eSTÓR project, described in this paper, focuses on the collection and curation of high-quality Irish text data for diverse domains.