Automatic post-editing (APE) systems aim to correct the systematic errors
made by machine translators. In this paper, we propose a neural APE system that
encodes the source (src) and machine translated (mt) sentences with two
separate encoders, but leverages a shared attention mechanism to better
understand how the two inputs contribute to the generation of the post-edited
(pe) sentences. Our empirical observations have showed that when the mt is
incorrect, the attention shifts weight toward tokens in the src sentence to
properly edit the incorrect translation. The model has been trained and
evaluated on the official data from the WMT16 and WMT17 APE IT domain
English-German shared tasks. Additionally, we have used the extra 500K
artificial data provided by the shared task. Our system has been able to
reproduce the accuracies of systems trained with the same data, while at the
same time providing better interpretability.

本文提出了一种神经自动后编辑系统，该系统使用两个独立编码器对源句子和机器翻译句子进行编码，并利用共享注意力机制更好地理解两个输入对于后编辑句子生成的贡献，其已在 WMT16 和 WMT17 APE IT domain 英德共享任务的官方数据以及附加的 500K 人工数据上进行了训练和评估，同时提供了更好的可解释性。