What has changed with neural machine translation? A critical review of human factors

Abstract

ABSTRACT In recent years, the push towards automation and translation productivity led to great efforts dedicated to the development of machine translation (MT) systems. Neural machine translation (NMT) represents the latest of these efforts. In this paper we present a critical review of human factors in NMT research with two goals: to provide a snapshot of research in NMT involving human stakeholders, and to appraise how professional translators have been included in discourses around NMT. We report four key findings. First, from translators’ perspective, changes brought about by the neural paradigm are not as much to do with workflows, but rather with the NMT editing process and its specifics. Second, the majority of NMT research involving human stakeholders is directed towards advancing the state of MT development rather than ensuring the usefulness of NMT as a tool for professionals. Third, the review suggested overall narrow conceptualisations of translation productivity that were often based solely on measures of processing time or throughput. Fourth, it emerged that NMT investigations involving end-users are still relatively scarce. We present and discuss these findings, and make recommendations for future research on topics including the concept of productivity and the role of NMT as a professional tool.