Can Large Language Models Do Simultaneous Machine Translation?
Monash University researchers show that large language models can do real-time machine translation and propose new ways for model fine-tuning.
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Monash University researchers show that large language models can do real-time machine translation and propose new ways for model fine-tuning.
Researchers from NVIDIA, Factored.ai, Talon Voice, and others open-source a properly licensed dataset of 1,780 hours of speech in 77 different languages, plus transcriptions.
YouTube Health launches THE-IQ Creator Program for healthcare video creators in four countries, including financing and pilot-phase access to the Aloud machine dubbing tool.
G/O Media fires Gizmodo’s entire staff of Spanish writers and begins publishing raw machine-translated versions of English articles that are now being indexed by Google Search.
DeepMind introduces a new method to improve the quality of large language models. Researchers choose machine translation as a use case to show how well the new approach works.
Language AI researchers show that fine-tuning large language models with fine-grained human judgment data boosts machine translation evaluation.
Longyue Wang on Tencent's approach to language technology and his research on the potential of large language models in more context-aware and personalized machine translation.
ADAPT researchers introduce adaptNMT, an innovative open-source application designed to simplify the development and deployment of machine translation models.
Federal Communications Commission’s proposal to expand language access is a good start, officials from 15 states say, but should include (human) translation into 25 languages.
MATEO aims to open up machine translation evaluation, making it accessible to more stakeholders and facilitating research, education, and critical evaluation of machine translation.
Boğaziçi University researchers reveal the potential of customizing MT systems to replicate a translator's style, leading to literary translations that mirror the translator's unique style.
A paper discussing the use of digital tools in academia addresses machine translation as both a problematic and beneficial tool, proposing increased awareness and clear policies for its use.
The Post-Edit Me! project supports educators in developing innovative post-editing training practices, fostering enhanced post-editing skills for the future of the industry.
Croatia, Estonia, Iceland, Latvia, and Malta are the first European countries to launch their own AI-powered National Language Technology Platform (NLTP).
Graham Neubig on the cost, performance, and potential applications of language models and their competitiveness versus special-purpose machine translation engines.
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