Large-scale machine translation evaluation of the iADAATPA project
Sheila Castilho, Natália Resende, Federico Gaspari, Andy Way, Tony O’Dowd, Marek Mazur, Manuel García–Herranz, Alexandre Helle, Gema Ramírez-Sánchez, Víctor M. Sánchez-Cartagena, Mārcis Pinnis, Valters Šics · Dublin City University Open Access Institutional Repository (Dublin City University) · 2019
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.