Like most machine learning applications, to get intelligent results from machine translation tools you need training data. We have reached the conclusion that more data is not always better. Instead of massive amounts of data, we need high-quality data, clustered for specific domains and content types...

Every company ‘sits’ on a mountain of language data in translation memories and content management systems. But that data are locked up in legacy formats and templates that make them not very useful and accessible in the modern scenarios of machine translation...

No more vision. No more predictions. The future is now. No need anymore to predict machine translation. It’s here and it’s working. Our message for the new year this time is therefore very pragmatic and single-minded: fair pay for the translators and data-keepers!

TAUS was founded in 2005 as a think tank with a mission to automate and innovate translation. Ideas transformed into actions. TAUS became the language data network offering the largest industry-shared repository of data, deep know-how in language engineering and a network of Human Language Project workers around the globe...

The need for more and better language data for machine translation and NLP model training has fueled the rise of annotation services over the last five years. In this webinar, we will talk about the business aspect of annotation services and data marketplaces...

At the end of October 2020, the first platform for language data acquisition - the Data Marketplace - went live. In order to encourage data sellers and buyers to enter the Data Marketplace, the platform offers advanced services aimed at facilitating the exploration and making the data on the marketplace MT-ready...

Access to large volumes of data is key to making advancements in machine translation technology. Another very important factor is the relevance of available data to the domain...

In this webinar, we take a strategic look at calculating the return on investment (ROI) when it comes to machine translation, in a three-year evaluation window. We explore a framework that is created based on a financial model commonly known as the NPV (Net Present Value)...

While the number of reports on increased quality and successful commercial usages of automated translations grows by the day, we wonder if the human parity has indeed been reached as claimed. The answer to this question lies in the way human parity is defined and assessed...

The DQF Dashboard has been designed to give you immediate insights into the most important metrics of your localization process. It shows the trends in your workflows over time, which languages in your production process are doing well, and which ones might need some extra attention...

The dream of an automated translation process has come closer to reality since the introduction of the neural networks. The promise of the neural systems is the potential to reach human-level performance with minimum human assistance when given enough training data. The result is translation that is fast, affordable and of good quality...

Through plugins in translation tools, DQF collects production and quality review data and generates extensive and detailed reports...

We invite our colleagues and TAUS followers from around the world to join us in a webinar to discuss the impacts of the corona crisis on our industry and business and to gather ideas and inspiration for collaborative actions that may bring relief.

Fighting this crisis is not the task of governments and public institutions alone. It is a battle that involves every citizen and every community on the planet. And so we ask: what can we as a language industry do?