Best Practices for Machine Translation Post-editing

The volume of content that requires translation is growing faster than we are able to handle with conventional means. Our content-driven world requires speed and agility, and Machine Translation Post-editing (MTPE) has proven to be the most effective way to meet the localization volume and quality demands of our times. With the advent of Neural Machine Translation (NMT), the practice of MTPE is set to continue to grow. 

Download this white paper to learn best practices for these key MTPE aspects: 

1. Content evaluation: Determining what content can successfully be processed using MTPE 
2. Model creation: Achieving the optimal MT solution and guaranteeing ROI 
3. MT output evaluation: Ensuring MT output is suitable for post-editing 
4. Machine translation post-editing: Processes for successful post-editing 
5. Quality assurance: Improving quality through feedback 
6. Neural Machine Translation: Leveraging the most powerful MT technology