The Fastest Machine Translation Framework Marian Boasts New and Impressive Features
Experts from Tilde, University of Edinburg, and Unbabel have improved the very popular automated neural translation toolkit Marian.
University of Edinburgh
Blazing-Fast Machine Translation With Kenneth Heafield
University of Edinburgh’s Kenneth Heafield joins the Pod to discuss translation efficiency, quality research, and industry vs. academia. Plus: Anna Wyndham on 10 areas where translators remain essential experts in the loop.
Here’s Europe’s Latest Attempt at End-to-End Speech Translation
European Live Translator (ELITR) takes speech-to-speech translation one step further, backed by the academe, private sector, and government.
Here Are the Top 10 Most Influential Research Papers on Neural Machine Translation
The most-cited neural machine translation research papers show how NMT came to dominate the field — and how academia and industry’s interests have evolved.
Facebook Patents Alternative to ‘Expensive’ BLEU
Facebook has been granted a patent for its machine translation quality evaluation system, which relies on user engagement data to decide which translation users prefer.
Slator 2019 Language Industry Market Report
Slator’s inaugural comprehensive language industry market report. It examines total market size, the 10 main end-buyer industries, competitive landscape, productivity tech, NMT and much more.
Researchers Run User Study on MT Post-Editing’s Most Promising Alternative
Researchers led by machine translation pioneer Philipp Koehn examine how interactive translation prediction compares to MT post-editing when powered by neural instead of statistical MT.
Google, Facebook, Amazon: Neural Machine Translation Just Had Its Busiest Month Ever
Research into neural machine translation in 2018 is exploding, with April 2018 being the busiest month ever for NMT, as Big Tech companies from Google to Facebook to Amazon go full throttle on multiple areas of research.
Business and Academia Join Forces to Launch Next-Gen Machine Translation
In 2015, four leading European institutions and companies set out to solve machine translation’s four main problems: training, context, scalability, and data collection. After a brief scare when Google Translate went neural, the project is now in beta and set for enterprise-grade release in Q4 2017.
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