Determining translators’ perception, productivity and post-editing effort when using SMT and NMT systems (2018)

Abstract: Thanks to the great progress seen in the machine translation (MT) field in recent years, the use and perception of MT by translators need to be revisited. The main objective of this paper is to determine the perception, productivity and the postediting effort (in terms of time and number of editings) of six translators when using Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) systems. This presentation is focused on how translators perceive these two systems in order to know which one they prefer and what type of errors and problems present each system, as well as how translators solve these issues. These tests will be performed with the Dynamic Quality Framework (DQF) tools (quick comparison and productivity tasks) using Google Neural Machine Translation and Microsoft Translator (SMT) APIs in two different English into Spanish texts, an instruction manual and a marketing webpage. Results showed that translators considerably prefer NMT over SMT. Moreover, NMT is more adequate and fluent than SMT.

## introduction machine translation (mt) is nowadays one of the most useful resources for translators and the translation industry. post-editing has become a usual practice within companies @xcite . with the great progress seen in nmt @xcite , there are still some problems to overcome when using it, © 2018 the authors. this article is licensed under a creative commons 3.0 license, no derivative works, attribution, cc-by-nd. ## aim of this proposal the aim of this paper is to determine the translators' perception when using smt and nmt, as well as to observe the differences when using smt and nmt based on the topic of the source text. the research questions addressed will be: