Introduction The language services industry is undergoing a profound transformation with the emergence of cutting-edge technologies such as ChatGPT and large language models (LLMs). These powerful language generation models have captivated the attention of businesses and language professionals alike, offering exciting possibilities for translation, localization, and content creation. In this article, we will explore the […]
The year is 2023. Six years after the big neural MT push of 2017, it seems appropriate to say that machine translation (MT) has finally found its way in the localization industry. Most MT providers are producing reasonably acceptable baseline quality and MT solutions have never been more accessible. As a result, MT is becoming a reality in many organizations. What’s more, MT technology has reached a certain level of maturity in terms of customization and training.
Today, machine translation (MT) is so pervasive that — for many young or early-career localization professionals, at least — it’s hard to imagine a time without it. But such a time did exist. Those with a decade or two of language industry experience under their belt have, no doubt, witnessed firsthand MT’s evolution into the nearly omnipresent entity that it is today.
Cologne-based DeepL has announced the beta launch of DeepL Write, an AI-powered authoring tool intended to improve texts by fixing errors and making suggestions for word replacements while keeping an eye on style, grammar and formatting.
Continuous improvement in machine translation (MT) technology means that MT engines are expected to get ever more effective. One of the areas where this is already happening is fuzzy matches for MT.
It is a given nowadays that when organizations expand internationally or consolidate their existing global presence, they need to invest in localization. This does not simply mean linguistic resources but also technological investments that contribute to translation productivity among other efficiencies.
On October 11, 2022, Google announced the launch of a new AI-powered cloud service called Translation Hub. The announcement instantly made waves in the language services industry and beyond, so Nimdzi took a closer look to see what’s really behind the new offering from the tech giant.
The Nimdzi Language Technology Atlas maps over 800 different technology solutions across a number of key product categories. The report highlights trends and things to watch out for. This is the only map you will ever need to navigate your way across the language technology landscape.
It’s already been six years now since Google revealed that Google Translate processes 146 billion words a day — three times more than what all the professional translators in the world combined can do in a month. That was 2016 and things haven’t really slowed down in the machine translation (MT) universe since.
There are nine categories of language technology and each plays an important role for a mature localization program. There is no better guest to have on the show to discuss this complex landscape than Yulia Akhulkova. Yulia Akhulkova is the foremost expert in the field of Language Technology categorization and evaluation. She is the lead […]