Large-scale machine translation evaluation of the iADAATPA project

This paper reports the results of an indepth evaluation of 34 state-of-the-art domain-adapted machine translation (MT) systems that were built by four leading MT companies as part of the EU-funded iADAATPA project. These systems support a wide variety of languages for several domains. The evaluation combined automatic metrics and human methods, namely assessments of adequacy, fluency, and comparative ranking. The paper also discusses the most effective techniques to build domain-adapted MT systems for the relevant language combinations and domains

Achievements of the PRINCIPLE project: promoting MT for Croatian, Icelandic, Irish and Norwegian

This paper provides an overview of the main achievements of the completed PRINCIPLE project, a 2-year action funded by the European Commission under the Connecting Europe Facility (CEF) programme. PRINCIPLE focused on collecting high-quality language resources for Croatian, Icelandic, Irish and Norwegian, which are severely low-resource languages, especially for building effective machine translation (MT) systems. We report the achievements of the project, primarily, in terms of the large amounts of data collected for all four low-resource languages and of promoting the uptake of neural MT (NMT) for these languages

Large-scale machine translation evaluation of the iADAATPA project

This paper reports the results of an indepth evaluation of 34 state-of-the-art domain-adapted machine translation (MT) systems that were built by four leading MT companies as part of the EU-funded iADAATPA project. These systems support a wide variety of languages for several domains. The evaluation combined automatic metrics and human methods, namely assessments of adequacy, fluency, and comparative ranking. The paper also discusses the most effective techniques to build domain-adapted MT systems for the relevant language combinations and domains