This paper describes the submission of the AMU (Adam Mickiewicz University)
team to the Automatic Post-Editing (APE) task of WMT 2016. We explore the
application of neural translation models to the APE problem and achieve good
results by treating different models as components in a log-linear model,
allowing for multiple inputs (the MT-output and the source) that are decoded to
the same target language (post-edited translations). A simple string-matching
penalty integrated within the log-linear model is used to control for higher
faithfulness with regard to the raw machine translation output. To overcome the
problem of too little training data, we generate large amounts of artificial
data. Our submission improves over the uncorrected baseline on the unseen test
set by -3.2\% TER and +5.5\% BLEU and outperforms any other system submitted to
the shared-task by a large margin.

本文探讨了神经机器翻译模型在自动后编辑任务上的应用，并借助对不同模型的处理提高结果，使用简单的字符串匹配惩罚来控制原始机器翻译输出，产生了大量人工数据来解决训练数据不足的问题，超越了基线和其他系统。