Translation Automation Society (TAUS) implements research results of Khalil Sima`an's DatAptor project

TAUS launches Matching Data: a new technique of selecting
language data for the training and tuning of machine
translation (MT) engines. This new approach is a perfect fit for
the new generation of Neural MT, which is much more sensitive to
the quality of the training data. Matching Data empowers MT
developers as well as Language Service Providers to efficiently
compile customized corpora for building their own domain-specific
translation solutions based on an example data set.

The DatAptor project was a research project undertaken by the Institute for Logic, Language and Computation of the
University of Amsterdam, led by Professor Khalil Sima’an and funded by the Dutch STW. Partners in the project were Intel, the Directorate General of Translation of the European Commission and TAUS. From 2013 to 2016 a team of researchers explored different approaches to make data selection from vast amounts of data seamless and more effective.