PosEdiOn: Post-Editing Assessment in PythOn

There is currently an extended use of postediting of machine translation (PEMT) in the translation industry. This is due to the increase in the demand of translation and to the significant improvements in quality achieved in recent years. PEMT has been included as part of the translation workflow because it increases translators' productivity and it also reduces costs. Although effective post-editing requires sufficiently high quality MT output, usual automatic metrics do not always correlate with post-editing effort. We describe a standalone tool designed both for industry and research that has two main purposes: to collect sentence-level information from the post-editing process (e.g. post-editing time and keystrokes) and to visually present multiple evaluation scores so they can be easily interpreted by a user.