News and analysis of the latest developments in machine translation, computer-aided-translation, natural language processing, and other language-related areas in artificial intelligence.
Technological advances are affecting the audiovisual-translation landscape, shaping a new skill set for professionals aspiring to compete in the AVT market; and these job profiles are here to stay
Japanese Government claims their machine translation engine has reached the level of professional translators specializing in the financial sector.
New EU-funded research assesses how effective machine translation is in literary translation; focuses on creative aspect of literary texts and reader experience.
Post-editors need specific competencies and skills to fulfill the requirements of a post-editing task — and establish post-editing as a profession in its own right.
Part of the billions of dollars going into the Metaverse will undoubtedly go into these two areas of machine translation. Meta AI reveals its MT roadmap and the people working on it.
Eye-tracking technology has advanced considerably such that use cases have opened up in machine translation evaluation, MT error identification, and pricing for post-edited MT.
After pushback, Switzerland’s national postal service reverses ban on employees using DeepL, Google Translate. Ban remains for PostFinance bank, which must rely on internal MT engine, Post Translate.
How necessary is parallel data for improving machine translation quality for low-resource languages? Google researchers scale model with ‘pragmatic approach.’
To be fair, this is the first-ever multilingual model to win the international machine translation contest. But major tech companies have been exploring multilingual models for years.
Studios release machine translated games and players run QA. Fan translations predate MT, but new tools may help gamers contribute to more seamless localizations.
New research from Memsource and Charles University in Prague finds connection between machine translation quality and post-editing speed weaker than previously assumed.
Amazon AI researchers delve deeper into automatic dubbing by controlling the length of machine translation output, aiming to automate “complex and demanding” dubbing workflow.
Researchers tracked CO2 emissions from training German, French, and English machine translation engines, and found this language pair to be most carbon-intensive.
Templates are to subtitles what automatic speech recognition (ASR) and machine translation (MT) are to the subtitling workflow: revolutionary. Experts test AppTek tool, share candid feedback.
Google researchers say Translatotron 2 outperforms original in terms of speech-to-speech translation quality and naturalness. Plus the new version prevents users from generating speech for deepfakes.
Combing through papers released from 2010 to 2020, researchers identify overreliance on metric BLEU to the exclusion of 100+ alternatives — a major weak point.
New paper by Facebook, Amazon, Twitter, and University of Melbourne examines vulnerability of systems using back translation to attacks on monolingual training data.
Facebook, Naver, and George Mason University release open-sourced metric based on Covid-19 terms; hope to bridge gap between academia and industry.
Google publishes translation dataset of biographies from Wikipedia in hopes of reducing gender bias in Google Translate output.
European Live Translator (ELITR) takes speech-to-speech translation one step further, backed by the academe, private sector, and government.
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