PE2 rr corpus: manual error annotation of automatically pre-annotated MT post-edits

We present a freely available corpus containing source language texts from different domains along with their automatically generated translations into several distinct morphologically rich languages, their post-edited versions, and error annotations of the performed post-edit operations. We believe that the corpus will be useful for many different applications. The main advantage of the approach used for creation of the corpus is the fusion of post-editing and error classification tasks, which have usually been seen as two independent tasks, although naturally they are not. We also show benefits of coupling automatic and manual error classification which facilitates the complex manual error annotation task as well as the development of automatic error classification tools. In addition, the approach facilitates annotation of language pair related issues.This publication has emanated from research supported by TRAMOOC project (Translation for Massive Open Online Courses) partially funded by the European Commission under H2020-ICT-2014/H2020-ICT-2014-1 under grant agreement number 644333 and by the Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (Insight).non-peer-reviewe