  The advantages of neural machine translation (NMT) have been extensively
validated for offline translation of several language pairs for different
domains of spoken and written language. However, research on interactive
learning of NMT by adaptation to human post-edits has so far been confined to
simulation experiments. We present the first user study on online adaptation of
NMT to user post-edits in the domain of patent translation. Our study involves
29 human subjects (translation students) whose post-editing effort and
translation quality were measured on about 4,500 interactions of a human
post-editor and a machine translation system integrating an online adaptive
learning algorithm. Our experimental results show a significant reduction of
human post-editing effort due to online adaptation in NMT according to several
evaluation metrics, including hTER, hBLEU, and KSMR. Furthermore, we found
significant improvements in BLEU/TER between NMT outputs and professional
translations in granted patents, providing further evidence for the advantages
of online adaptive NMT in an interactive setup.
