The Great ChatGPT and Translation Debate
Adam Bittlingmayer, Varshul Gupta, and Mihai Vlad on LLMs and machine translation, client perspectives, state-of-the-art research, and GPT’s impact on the language industry.
Language Industry (Artificial) Intelligence — Slator Answers
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Why Quality Estimation Should Be Fundamental to Machine Translation
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.
At SlatorCon Remote, What to Focus on Amid All the ‘Wait and See’
SlatorCon Remote September 2022 saw the biggest percentage of buy-side participants of any SlatorCon to date. Here are the highlights.
As Machine Translation Gains Ground, It Taints Training Data — New Research Shows
Is machine translation trained mostly on...machine translation? Research sparks discussion among MT experts. Quality issues found to be likely worse for non-English pairs, low-resource languages.
Is TensorFlow ‘for Boomers’? PyTorch May Gain More Ground in Machine Translation
Developers praise PyTorch, Facebook’s open source framework, for its flexibility, speed, and usability. But the machine translation world is not ready to dethrone TensorFlow just yet.
What’s so Massive About Google’s Massively Multilingual Neural Machine Translation?
New research by Google AI explores multilingual NMT on an unprecedented scale. Good for low-resource languages, less so for the rest, results show.
How Language Featured at the 2019 Applied Machine Learning Days
Language industry startups Unbabel and Lilt took to the stage at one of Europe’s largest machine learning conferences and explained how they build atop evermore sophisticated AI tech.
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