A roadmap to neural automatic post-editing: an empirical approach

Dimitar Shterionov*, Félix do Carmo, Joss Moorkens, Murhaf Hossari, Joachim Wagner, Eric Paquin, Dag Schmidtke, Declan Groves, Andy Way

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

In a translation workflow, machine translation (MT) is almost always followed by a human post-editing step, where the raw MT output is corrected to meet required quality standards. To reduce the number of errors human translators need to correct, automatic post-editing (APE) methods have been developed and deployed in such workflows. With the advances in deep learning, neural APE (NPE) systems have outranked more traditional, statistical, ones. However, the plethora of options, variables and settings, as well as the relation between NPE performance and train/test data makes it difficult to select the most suitable approach for a given use case. In this article, we systematically analyse these different parameters with respect to NPE performance. We build an NPE “roadmap” to trace the different decision points and train a set of systems selecting different options through the roadmap. We also propose a novel approach for APE with data augmentation. We then analyse the performance of 15 of these systems and identify the best ones. In fact, the best systems are the ones that follow the newly-proposed method. The work presented in this article follows from a collaborative project between Microsoft and the ADAPT centre. The data provided by Microsoft originates from phrase-based statistical MT (PBSMT) systems employed in production. All tested NPE systems significantly increase the translation quality, proving the effectiveness of neural post-editing in the context of a commercial translation workflow that leverages PBSMT.

Original languageEnglish
Pages (from-to)67-96
Number of pages30
JournalMachine Translation
Volume34
Issue number2-3
DOIs
Publication statusPublished - 1 Sep 2020

Keywords

  • Automatic post-editing
  • Deep learning
  • Empirical evaluation
  • Machine Translation
  • Multi-source
  • Neural post-editing

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    Shterionov, D., Carmo, F. D., Moorkens, J., Hossari, M., Wagner, J., Paquin, E., Schmidtke, D., Groves, D., & Way, A. (2020). A roadmap to neural automatic post-editing: an empirical approach. Machine Translation, 34(2-3), 67-96. https://doi.org/10.1007/s10590-020-09249-7