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.

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.

The rapid evolution of artificial intelligence (AI) has introduced AI agents as a disruptive paradigm across various industries, yet their application in machine translation (MT) remains underexplored. This paper describes and analyses the potential of single- and multi-agent systems for MT, reflecting on how they could enhance multilingual digital communication. While single-agent systems are well-suited for simpler translation tasks, multi-agent systems, which involve multiple specialized AI agents collaborating in a structured manner, may offer a promising solution for complex scenarios requiring high accuracy, domain-specific knowledge, and contextual awareness. To demonstrate the feasibility of multi-agent workflows in MT, we are conducting a pilot study in legal MT. The study employs a multi-agent system involving four specialized AI agents for (i) translation, (ii) adequacy review, (iii) fluency review, and (iv) final editing. Our findings suggest that multi-agent systems may have the potential to significantly improve domain-adaptability and contextual awareness, with comparable translation quality to traditional MT or single-agent systems. This paper also sets the stage for future research into multi-agent applications in MT, integration into professional translation workflows, and shares a demo of the system analyzed in the paper.

Clear communication between patients and healthcare providers is crucial, particularly in informed consent forms (ICFs), which are often written in complex, technical language. This paper explores the effectiveness of generative artificial intelligence (AI) for simplifying ICFs into Plain Language (PL), aiming to enhance patient comprehension and informed decision-making. Using a corpus of 100 cancer-related ICFs, two distinct prompt engineering strategies (Simple AI Edit and Complex AI Edit) were evaluated through readability metrics: Flesch Reading Ease, Gunning Fog Index, and SMOG Index. Statistical analyses revealed statistically significant improvements in readability for AI-simplified texts compared to original documents. Interestingly, the Simple AI Edit strategy consistently outperformed the Complex AI Edit across all metrics. These findings suggest that minimalistic prompt strategies may be optimal, democratizing AI-driven text simplification in healthcare by requiring less expertise and resources. The study underscores the potential for AI to significantly improve patient-provider communication, highlighting future research directions for qualitative assessments and multilingual applications.

This paper presents a user study with 11 professional English-Spanish translators in the legal domain. We analysed whether negative or positive translators’ pre-task perceptions of machine translation (MT) being an aid or a threat had any relationship with final translation quality and productivity in a post-editing workflow. Pre-task perceptions of MT were collected in a questionnaire before translators conducted post-editing tasks and were then correlated with translation productivity and translation quality after an Adequacy-Fluency evaluation. Each participant translated 13 texts over two consecutive weeks, accounting for 120,102 words in total. Results show that translators who had higher levels of trust in MT and thought that MT was not a threat to the translation profession reported higher translation quality and productivity. These results have critical implications: improving translator-computer interactions and fostering MT literacy in translation training may be crucial to reducing negative translators’ pre-task perceptions, resulting in better translation productivity and quality, especially adequacy.

Perceptions and experiences of machine translation (MT) users before, during, and after their interaction with MT systems, products or services has been overlooked both in academia and in industry. Tradi-tionally, the focus has been on productivi-ty and quality, often neglecting the human factor. We propose the concept of Ma-chine Translation User Experience (MTUX) for assessing, evaluating, and getting further information about the user experiences of people interacting with MT. By conducting a human-computer in-teraction (HCI)-based study with 15 pro-fessional translators, we analyse which is the best method for measuring MTUX, and conclude by suggesting the use of the User Experience Questionnaire (UEQ). The measurement of MTUX will help eve-ry stakeholder in the MT industry - devel-opers will be able to identify pain points for the users and solve them in the devel-opment process, resulting in better MTUX and higher adoption of MT systems or products by MT users.

Detection of occupations in texts is relevant for a range of important application scenarios, like competitive intelligence, sociodemographic analysis, legal NLP or health-related occupational data mining. Despite the importance and heterogeneous data types that mention occupations, text mining efforts to recognize them have been limited. This is due to the lack of clear annotation guidelines and high-quality Gold Standard corpora. Social media data can be regarded as a relevant source of information for real-time monitoring of at-risk occupational groups in the context of pandemics like the COVID-19 one, facilitating intervention strategies for occupations in direct contact with infectious agents or affected by mental health issues. To evaluate current NLP methods and to generate resources, we have organized the ProfNER track at SMM4H 2021, providing ProfNER participants with a Gold Standard corpus of manually annotated tweets (human IAA of 0.919) following annotation guidelines available in Spanish and English, an occupation gazetteer, a machine-translated version of tweets, and FastText embeddings. Out of 35 registered teams, 11 submitted a total of 27 runs. Best-performing participants built systems based on recent NLP technologies (e.g. transformers) and achieved 0.93 F-score in Text Classification and 0.839 in Named Entity Recognition. Corpus: https://doi.org/10.5281/zenodo.4309356