Shterionov Dimitar, Carmo Félix do, Moorkens Joss, Hossari Murhaf, Wagner Joachim, Paquin Eric, Schmidtke Dag, Groves Declan, Way Andy
Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands.
ADAPT Centre, School of Computing, Dublin City University, Dublin, Ireland.
Mach Transl. 2020;34(2):67-96. doi: 10.1007/s10590-020-09249-7. Epub 2020 Sep 3.
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.
在翻译工作流程中，机器翻译（MT）之后几乎总会有一个人工后编辑步骤，即将原始的机器翻译输出进行校正，以达到所需的质量标准。为了减少人工翻译需要校正的错误数量，自动后编辑（APE）方法已被开发并应用于此类工作流程中。随着深度学习的发展，神经自动后编辑（NPE）系统已经超越了更为传统的统计型系统。然而，大量的选项、变量和设置，以及NPE性能与训练/测试数据之间的关系，使得为特定用例选择最合适的方法变得困难。在本文中，我们针对NPE性能系统地分析了这些不同参数。我们构建了一个NPE“路线图”来追踪不同的决策点，并通过该路线图训练一组选择不同选项的系统。我们还提出了一种带有数据增强的新型APE方法。然后，我们分析了其中15个系统的性能，并找出了最佳系统。事实上，最佳系统是那些采用新提出方法的系统。本文所介绍的工作源自微软与ADAPT中心的一个合作项目。微软提供的数据源自生产中使用的基于短语的统计机器翻译（PBSMT）系统。所有经过测试的NPE系统都显著提高了翻译质量，证明了神经后编辑在利用PBSMT的商业翻译工作流程中的有效性。
