Abstract
Neural machine translation is increasingly being promoted and introduced in the field of translation, but research into its applicability for post-editing by human translators and its integration within existing translation tools is limited. In this study, we compare the quality of SMT and NMT output of the commercially-available interactive and adaptive translation environment Lilt, as well as the translation process of professional translators working with both versions of the tool, their preference for SMT vs. NMT for post-editing, and their attitude towards such an interactive and adaptive translation tool compared to their usual translation environments.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
