It’s a well-known truism that poorly written source has a profound negative effect on the quality of machine translation, drastically reduces the productivity of post-editors and impacts turnaround times. But what is bad and how bad is bad? Conversely, what are the features emblematic of good content and how good is good? The impact of source on MT is crucial since a lot of content is written by non-native authors, created by technical specialists for a non-technical audience and may not adhere to brand tone and voice. AI can be employed to identify these errors and predict ‘at-risk’ content prior to localization in a multitude of languages. The presentation will show how source files and even individual sentences within those source files can be analyzed for markers of complexity and readability and thus are more likely to cause mistranslations and omissions for machine translation and subsequent post-editing. Potential solutions will be explored such as rewriting the source to be in line with acceptable threshold criteria for each product and/or domain, re-routing to other machine translation engines better suited for the task at hand and building AI-based predictive models.

In the last couple of years, machine translation technology has seen major changes with the breakthrough of neural machine translation (NMT), a growing number of providers and translation platforms. Machine Translation generally is experiencing a peak in demand from translation buyers, thanks to Machine Learning and AI being omnipresent in the media and at industry events. At the same time, new models for defining translation quality are becoming more widely adopted. These changes have profound implications for translators, LSPs and translation buyers: translators have to adjust their post-editing approaches, while LSPs and translation buyers are faced with decisions on selecting providers, best approaches for updating MT systems, financial investments, integrating tools, and getting the timing for implementation right for an optimum ROI.In this tutorial on MT and post-editing we would like to continue sharing the latest trends in the field of MT technologies, and discuss their impact on post-editing practices as well as integrating MT on large, multi-language translation programs. We will look at tool compatibility, different use cases of MT and dynamic quality models, and share our experience of measuring performance.

This paper describes PROMT system deployment at PayPal including: PayPal localization process challenges and requirements to a machine translation solution; Technical specifications of PROMT Translation Server Developer Edition; Linguistic customization performed by PROMT team for PayPal; Engineering Customization performed by PROMT team for PayPal; Additional customized development performed by PROMT team on behalf of PayPal; PROMT engine and PayPal productivity gains and cost savings.