A user study of neural interactive translation prediction

Rebecca Knowles,Philipp Koehn,Marina Sanchez-Torron

doi:10.1007/s10590-019-09235-8

Rebecca Knowles, Philipp Koehn + Show 1 more

Open Access

https://doi.org/10.1007/s10590-019-09235-8

Copy DOI

Abstract

Machine translation (MT) on its own is generally not good enough to produce high-quality translations, so it is common to have humans intervening in the translation process to improve MT output. A typical intervention is post-editing (PE), where a human translator corrects errors in the MT output. Another is interactive translation prediction (ITP), which involves an MT system presenting a translator with translation suggestions they can accept or reject, actions the MT system then uses to present them with new, corrected suggestions. Both Macklovitch (2006) and Koehn (2009) found ITP to be an efficient alternative to unassisted translation in terms of processing time. So far, phrase-based statistical ITP has not yet proven to be faster than PE (Koehn 2009; Sanchis-Trilles et al. 2014; Underwood et al. 2014; Green et al. 2014; Alves et al. 2016; Alabau et al. 2016). In this paper we present the results of an empirical study on translation productivity in ITP with an underlying neural MT system (NITP). Our results show that over half of the professional translators in our study translated faster with NITP compared to PE, and most preferred it over PE. We also examine differences between PE and ITP in other translation productivity indicators and translators’ reactions to the technology.

Highlights

Interactive translation prediction (ITP) serves to allow translators to work with the output of machine translation (MT) systems by using it like an “auto-complete” feature
As described in Wuebker et al (2016) and Knowles and Koehn (2016), this is done in neural interactive translation prediction (NITP) by feeding the translator’s token(s) into the neural machine translation (NMT) model as conditioning context, producing the rest of the translation token by token
Using reference text to simulate translators, both papers show that NITP outperforms ITP systems that are based on phrase-based statistical MT even when the underlying MT systems are of similar quality

Summary

Introduction

Interactive translation prediction (ITP) serves to allow translators to work with the output of machine translation (MT) systems by using it like an “auto-complete” feature. Rather than starting with a complete (but likely erroneous) translation which they must post-edit (PE), a translator using ITP guides the translation process. They can accept a suggestion with a single keystroke, or reject it by typing an alternate translation. Using reference text to simulate translators, both papers show that NITP outperforms ITP systems that are based on phrase-based statistical MT even when the underlying MT systems are of similar quality

Objectives

Results

Conclusion