Here’s What Happened at the ‘New Trends in Translation and Technology’ Conference
NeTTT conference brings together academia, industry, and EU institutions, highlights new trends in translation and technology.
NeTTT conference brings together academia, industry, and EU institutions, highlights new trends in translation and technology.
Natural language processing scientists from Lilt train model on human translation errors. The goal, to automate the correction of human translations.
NLP technologies drive the evolution of localization, turning jargon into common terms used to manage today’s ops and tomorrow’s strategic plans. Here are five NLP terms.
Meta eliminates text generation in speech to speech translation; shares code and research with the public.
Large pretrained language models (PLMs) memorize lots of personal data. Researchers examine data privacy risks associated with PLMs, propose solutions.
Lingua Custodia Lab team present their NLP research article “Approach to integrating terminology constraints into neural translation models” which has been selected for publication.
Could a mouse-less, keyboard-less future be in the cards for translators? Slator readers weigh in on this and other developments in language technology.
Amazon presents three ways it hopes to extend current natural language processing R&D beyond just a small subset of the world's 7,000+ languages.
NeuralSpace CEO, Felix Laumann, joins the pod to discuss how they overcome the lack of research, data, and tools when training NLP models in low-resource languages.
SaaS platform, designed for software and app developers with limited knowledge of NLP, valued at USD 11m; advocates “voice-first” approach for end-users in developing countries.
Meta shoots for completing world’s fastest supercomputer by mid-2022. Here’s what it can do in terms of natural language processing and translation — and what the facility looks like today.
As more language service providers explore data collection and annotation, a new study shows large language models generate more diverse content, while humans excel at rating it.
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.
Indian language human+AI translation platform, Devnagri, raises USD 600,000 to help businesses reach one billion Indians in their own regional language. CEO reveals what’s next after text translation.
Unpacking DeepL’s turbo growth; and Welocalize AI Innovation VP, Olga Beregovaya, joins the Pod to discuss the evolution of NLP, the state of AI, and global content transformation.
Facebook sees textless natural language processing (NLP) as rendering automatic speech translation (ASR) obsolete by working in true end-to-end fashion: from speech input to speech output.
Switzerland-based machine translation provider, TextShuttle, promotes Software Engineer, Lucas Seiler, to CEO. Seiler on their business model and growth opportunities.
Translators should start learning how to write computer code, said the majority of poll respondents. But nearly half believe that creative machine translation is a non-starter.
Students of linguistics will likely run into Python, the most popular programming language for learning translation technology today given its user-friendliness and ubiquity.
What makes a buyer stay with a language service provider for over 17 years? Dell’s Wayne Bourland and Welocalize’s Darin Goble on the longstanding partnership between the two companies.