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Panelists from ServiceNow, LanguageWire, Busch Vacuum Solutions weighed the risks and opportunities of employing large language models in practical enterprise localization workflows.
Language AI researchers show that fine-tuning large language models with fine-grained human judgment data boosts machine translation evaluation.
Logrus Global and the University of Manchester showcase that fine-tuning LLMs on historical post-editing data can pinpoint segments that require editing and predict translation quality.
Research shows that incorporating preparatory steps and self-generated knowledge into the translation process of LLMs enables them to emulate human translation strategies.
Researchers from Tilburg University boost machine translation quality estimation with domain adaptation and data augmentation.
Microsoft introduces a GPT-based metric to evaluate translation quality and highlights the state-of-the-art capabilities of large language models (LLMs) in this task.
60-page report on interaction between human experts and AI in translation production; includes AI-enabled workflows, adoption rates, postediting, pricing models.
What you need to know about machine translation quality estimation (MTQE) — from benefits and challenges to industry adoption — as discussed by Adam Bittlingmayer and Conchita Laguardia.
Stunning majority of poll respondents think regulators should require flagging of machine translations. Plus your take on fully-automated translation quality reviews, etc.
Unbabel CEO Vasco Pedro joins SlatorPod to talk about machine translation for multilingual customer service, hiring top AI talent, the Lisbon startup scene, and learning from investors.
Memsource CEO David Čaněk talks about the recent acquisition and integration of Phrase and the future of AI and MT technology in shaping the localization industry.
New research suggests biases of QE datasets lead to guesstimates on machine translation quality. Researchers propose modified dataset, debut it at WMT 2020.
Amazon Prime Video researchers have developed a new quality estimation system for subtitle translations, which tells you when post-editing or rewrites are needed.
From technology, markets, and M&A to investment and the competitive landscape, the language industry leaders we interviewed in 2019 commented on a wide range of issues. Here are our top picks.
Characterizing post-editors based on their keystrokes, mouse activity, and other editing actions can help predict speed and, eventually, quality, says a new paper by Unbabel researchers.
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