Consistency is one of the desired quality features in final translations. For human-only translations (without MT), we rely on the translator’s ability to achieve consistency. For MT, consistency is neither guaranteed nor expected. MT may actually generate inconsistencies, and it is left to the post-editor to introduce consistency in a manual fashion. This work presents a method that facilitates the improvement of consistency without the need of a glossary. It detects inconsistencies in the post-edited work, and gives the post-editor the opportunity to fix the translation towards consistency. We describe the method, which is simple and involves only a short Python script, and also provide numbers that show its positive impact. This method is a contribution to a broader set of quality checks that can improve language quality of human and MT translations.

Interested in being more confident about the quality of your post-edited data? This is a session to learn how to create a Longitudinal Review that looks at specific aspects of quality in a systematic way, for the entire content and not just for a sample. Are you a project manager for a multilingual project? The Longitudinal Review can give insights to help project management, even if you are not a speaker of the target language. And it can help you detect issues that a Sample Review may not detect. Please come learn more about this new way to look at review.