基于翻译质量估计的神经网络译文自动后编辑
Neural Post-Editing Based on Machine Translation Quality Estimation

摘要 针对译文后编辑中的过度修正问题, 提出利用神经网络自动后编辑方法, 训练专门用于提供少量复合编辑修正和单一编辑类型修正的神经网络后编辑模型。在此基础上, 通过建立一个基于翻译质量估计的译文筛选算法, 将提出的模型与常规的神经网络自动后编辑模型进行联合。在WMT16自动后编辑任务测试集上的实验结果表明, 与基准系统相比, 所提方法显著提高了机器译文的翻译质量, 实验分析也表明该方法能有效地处理过度修正造成的译文质量下降问题。
Abstract In order to solve the problem of overcorrection in automatic post-editing translations, the authors propose to make advantage of the neural post-editing (NPE) to build two special models: one is used to provide minor edit operations, the other is used to provide single edit operation, and make advantage of machine translation quality estimation to establish a filtering algorithm to integrate the special models with the regular NPE model into a jointed model. Experimental results on the test set of WMT16 APE shared task show that the proposed approach statistically outperforms the baseline. Deep analysis further confirms that proposed approach can bring considerable relief from the over-editing problem in APE.