A new arXiv paper analyzes how value is defined and negotiated across the increasingly automated language and translation sector. It draws on interviews with 29 industry stakeholders gathered through the LT-LiDER project. The author discusses what these stakeholder perspectives could mean for how translation is taught.
North Small Translate is a new open-weight translation model that also follows instructions, described as being trained on the same base as Cohere's Command A Plus mixture-of-experts system with 25 billion active parameters. The authors position it as a cost-effective option for machine translation workloads that need instruction-following behaviour.
A new arXiv paper introduces CHAI, a reinforcement learning approach that uses AI-generated feedback to boost large language models' handling of code-mixed text. The authors note that while LLMs perform well on many tasks, they remain weak at understanding code-mixed language, and that this gap has drawn little research attention. The work targets code-mixed translation as a testbed for the method.
A new arXiv paper examines how to decide which machine translations of classical texts need expert review when no human reference translation exists. Using Pali-to-English as a test case, it combines source-novelty measures, GEMBA-style quality scoring, and a limited review budget. The goal is a practical way to route scarce expert attention to the most error-prone outputs.
Researchers detail Rosetta, their entry for Subtask 1 of the AlexandriaX shared task, which covers context-aware translation of English dialogue into dialectal Arabic, competing in both the constrained and unconstrained tracks. The system applies a LoRA adapter fine-tuned on top of NileChat to handle dialect variation in conversational translation.
A new arXiv paper draws on third-wave Extended Mind theory and radical enactivism to argue against models that treat cognition as manipulation of internal representations. The authors introduce an ABC framework in which translation is framed as an enacted process of the translating mind rather than symbolic computation. The work appears in the computation and language category as a revised version.
A new arXiv paper introduces NOPE-HYPE, a structured simulation workflow for examining how speech-to-text translation systems perform under a wide range of acoustic conditions. The authors argue that large speech models remain sensitive to environments they have not encountered and that current pipelines lack controllable tools for exploring such scenarios. The workflow offers researchers a systematic way to probe model robustness before deployment.
A computational linguistics paper investigates how readers actually experience AI-translated literary works, looking beyond surface accuracy to immersion and literary impact. The findings suggest that while machine translations convey the content acceptably, readers still favor human versions, indicating that common automatic evaluation metrics overlook qualities that matter most in literary reading.