If you’ve ever copied a string of foreign text into Google Translate, you’re surely familiar with the Detect Language function of neural machine translation, the form of language AI that delivers everything from a quick lookup in another language to the raw MT in any machine translation post-editing (MTPE) localization project. Even if you don’t […]
Disruptive So Far: Looking Back at Neural Machine Translation (NMT) Within Natural Language Processing
With news this week that Google has improved some of the key linguistic functionalities of its search engine’s machine learning algorithms – including an AI spell check improvement that Google’s head of search considers more important by itself than the previous five years’ progress – it is an interesting moment in the brief history of […]
The industry is projected to continue an upwards trend in growth as more companies utilize localization strategies to upgrade their multinational operations, with many allocating line item budgets specifically for localization. As consumers move away from traditional forms of media consumption, the industry will see a greater demand for video and voice localization for a range of devices and platforms, and embrace newer methods to improve agility and adaptability. Humans will still remain relevant since AI-backed advancements in machine translation technology, however remarkable, will not spell doom for LSPs and translators; instead, their adoption will popularize transcreation services and specialized content skills.
Pop culture references have always evaded dictionaries for some time after their initial adoption, and now due to the voracity and speed at which the internet consumes and discards new slang and references, machines can’t quite keep up. Ayan has pointed to “odd spellings, hashtags, urban slang, dialects, hybrid words, and emoticons” as being the major hurdles for NMT.