Custom.MT Console has rolled out a key update: a way to combine the output of popular machine translation engines with generative AI. Localization specialists may now easily style MT output and “mix and blend colors” to get a more accurate and evocative machine translation that speaks with the audience. Machine translation engines are reliable but […]
Localization teams in 2024 have a renewed interest in implementing AI translation in workflows to trim their budgets and improve per-word costs. However, monetization often becomes a struggle because the mechanism to track human effort fairly and responsively is underdeveloped. Here is a way to tackle this challenge. Value for Localization Programs Machine translation has […]
On Monday, November the 6th, OpenAI unveiled improved models: The release is a gauntlet of challenge thrown to a broad range of AI companies, not just LLM builders like Antropic and Cohere, but also speech recognition, synthetic voice, and machine translation companies. OpenAI positioned itself as a future one-stop shop for models. While getting image, […]
Custom.MT, a leading provider of applied AI for localization, has been approved for Nvidia’s Inception program for startups. With this program, Nvidia supports and nurtures promising startups in the fields of AI and data science. As a member of Inception, Custom.MT will have access to significant benefits that will help us develop custom models for […]
Custom.MT integrated ChatGPT and made it available through a Trados Plugin. Try it, the first 50,000 characters are on us. Like all technology teams out there, Custom.MT is experiencing the AI revolution with awe and excitement. As soon as OpenAI made waves, we decided to make it work for localization people. Working on our first […]
This guide is useful to train your own Microsoft AutoML translation model. For example, you can train a domain model, such as medical, legal, video games, financial reporting with your translation memory accumulated over the years. Alternatively, you can make an organization-specific model that knows all the product and people names, and follows your individual […]
This guide is useful to train your own Google AutoML translation model. For example, you can train a domain model, such as medical, legal, video games, financial reporting with your translation memory accumulated over the years. Alternatively, you can make an organization-specific model that knows all the product and people names, and follows your individual […]
This guide is useful to train your own Amazon translation model. For example, you can train a domain model, such as medical, legal, video games, financial reporting with your translation memory accumulated over the years. Alternatively, you can make an organization-specific model that knows all the product and people names, and follows your individual styles. […]
Running professional human evaluations of machine translation requires detailed methodology and software tools to streamline the process. The most detailed approach to evaluation today is to label errors and assign weights to critical issues using a variation of DQF/MQM ontology. In this article, we outline Custom.MT’s journey to selecting and implementing a tool for human […]
Founded in 2005 as a think tank, TAUS has turned into a prominent marketplace for machine translation training datasets, and a data services provider. Gone are the days of hosting conferences, the TAUS team is now busy collecting text and speech data for the likes of Google and Microsoft in a crowded space alongside Appen, […]
Machine translation is an important field that connects humans across the planet, giving each person unprecedented access to information. Yet very little is published about the people who drive the industry forward. Starting out in MT, we realized that neither the business nor the scientific communities have a clear vision of who the movers and shakers in the industry are today.
We’re seeing a rise in the number of projects and requests that rely on raw machine translation. When Custom.MT started, clients wanted to train models to improve the productivity of human translators, and to reduce the cost. Today, more than half of the requests we get are about doing the translation fully automatically, without a […]