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

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.

Re-thinking Machine Translation Post-Editing Guidelines

“…In the extensive literature on the subject of translators' attitudes towards MTPE (Blagodarna 2018;Cadwell et al 2018;Ginovart et al 2020;Ragni and Vieira 2022;Vieira 2018;Teixiera and O'Brien 2018, among others) there is little mention of how translators perceive MTPE guidelines or how these affect the way the task is performed. Factors affecting attitudes refer to price, productivity, effort or cognitive load, but guidelines are seldom mentioned.…”

Large language models "ad referendum": How good are they at machine translation in the legal domain?

“…Although quality evaluation has remained the same with the use of NMT (Ragni & Vieira 2022), we have yet to examine if that can be performed in the same way with LLMs, as there is a potential issue of "existing evaluation metrics may not be sufficient to capture the full range of translation quality" (Lyu et al 2023: 4). For this reason, different works approaching translation with LLMs can provide insights in carrying out the evaluation.…”

Translation Performance from the User’s Perspective of Large Language Models and Neural Machine Translation Systems

“…With the development of AI technology, interest in artificial neural network machine translation has increased. However, although the quality of machine translation has significantly improved, the quality of human translation remains superior [17].…”