Does NMT make a difference when post-editing closely related languages? The case of Spanish-Catalan

Abstract

In the last years, we have witnessed an increase in the use of post-editing of machine translation (PEMT) in the translation industry. It has been included as part of the translation workflow because it increases productivity of translators. Currently, many Language Service Providers offer PEMT as a service. For many years now, (closely) related languages have been post-edited using rulebased and phrase-based machine translation (MT) systems because they present less challenges due to their morphological and syntactic similarities. Given the recent popularity of neural MT (NMT), this paper analyzes the performance of this approach compared to phrase-based statistical MT (PBSMT) on in-domain and general domain documents. We use standard automatic measures and temporal and technical effort to assess if NMT yields a real improvement when it comes to post-editing the Spanish-Catalan language pair.