Incremental Adaptation of NMT for Professional Post-editors: A User Study
This incremental improvement benefits professional translators by enhancing machine translation post-editing efficiency.
The study tackled the problem of adapting neural machine translation systems to new bilingual data generated during human post-editing, resulting in reduced human effort, improved translation quality, and positive user feedback.
A common use of machine translation in the industry is providing initial translation hypotheses, which are later supervised and post-edited by a human expert. During this revision process, new bilingual data are continuously generated. Machine translation systems can benefit from these new data, incrementally updating the underlying models under an online learning paradigm. We conducted a user study on this scenario, for a neural machine translation system. The experimentation was carried out by professional translators, with a vast experience in machine translation post-editing. The results showed a reduction in the required amount of human effort needed when post-editing the outputs of the system, improvements in the translation quality and a positive perception of the adaptive system by the users.