论文标题
计算机辅助翻译，具有神经质量估计和自动后编辑
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing
论文作者
论文摘要
随着神经机器翻译的出现，朝着利用和消耗机器翻译结果的明显转变。但是，机器翻译系统和人类翻译器之间的差距需要通过后编辑手动封闭。在本文中，我们提出了一个端到端的深度学习框架，即质量估计和机器翻译输出的自动后编辑。我们的目标是提供错误纠正建议，并通过可解释的模型进一步减轻人类翻译人员的负担。为了模仿人类翻译人员的行为，我们设计了三个有效的委托模块 - 质量估算，生成后编辑和原子操作后编辑后编辑并构建基于它们的分层模型。我们使用英语 - WMT 2017 APE共享任务的英语数据集检查了这种方法，我们的实验结果可以实现最新的性能。我们还验证了经认证的翻译人员可以通过人类评估中的模型大幅加快其后编辑处理。
With the advent of neural machine translation, there has been a marked shift towards leveraging and consuming the machine translation results. However, the gap between machine translation systems and human translators needs to be manually closed by post-editing. In this paper, we propose an end-to-end deep learning framework of the quality estimation and automatic post-editing of the machine translation output. Our goal is to provide error correction suggestions and to further relieve the burden of human translators through an interpretable model. To imitate the behavior of human translators, we design three efficient delegation modules -- quality estimation, generative post-editing, and atomic operation post-editing and construct a hierarchical model based on them. We examine this approach with the English--German dataset from WMT 2017 APE shared task and our experimental results can achieve the state-of-the-art performance. We also verify that the certified translators can significantly expedite their post-editing processing with our model in human evaluation.
