Get Ready for MT Summit 2025: Highlights and Expectations
The MT Summit brings together researchers, developers, and language industry professionals to focus on machine...
#machine translation
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Google Calls for Rethink of Single-Metric AI Translation Evaluation
Google and Imperial College researchers propose evaluating AI translation systems on an accuracy-naturalness plane instead...
Language AI Briefing April 2025
The language industry’s definitive monthly AI briefing; top AI stories, quote of the month, a...
AI Translation Improves for Indic Languages. Preserving Sentiment Still a Challenge
A new study finds that while large language models improve translation accuracy for Indic texts,...
How Large Language Models Improve Document-Level AI Translation
University of Zurich and Huawei researchers show how LLMs improve document-level AI translation by preserving...
GTS 2025 MTPE Survey: What Translators Really Think About Machine Translation Tools
GTS Translation Services has released the results of its 2025 industry survey on Machine Translation Post-Editing...
Multi-Agent AI Framework Aims to Improve Cultural Adaptation in AI Translation
Researchers from Shahjalal University of Science and Technology and the University of Oklahoma propose a...
How France’s Inria Aims to Improve AI Translation for Low-Resource Languages
Inria improves AI translation for low-resource languages by breaking sentences into simpler phrases, translating them...
Translationese Remains a Challenge for Large Language Models, Study Finds
A new study finds that translationese — unnatural, overly literal translations — remains a persistent...
Unbabel Tackles Metric Bias in AI Translation
Unbabel exposes how using the same metrics for both training and evaluation can create misleading...
How to Balance Cost and Quality in AI Translation Evaluation
Researchers show how to reduce computational overhead in LLM-based translation evaluation while maintaining accuracy, offering...