神经机器翻译的无监督质量估计（CS CL）---刘子蔚

质量估计（QE）是使机器翻译（MT）在实际应用中有用的重要组成部分，因为它旨在在测试时告知用户MT输出的质量。现有方法需要大量的专家注释数据，计算和训练时间。作为替代方案，我们设计了一种无监督的QE方法，除MT系统本身外，无需培训或访问其他资源。与当前大多数将MT系统视为黑匣子的工作不同，我们探索了可以从MT系统中提取的有用信息，这些信息是翻译的副产品。通过采用不确定性量化的方法，我们可以与人类对质量的判断取得很好的相关性，可以与最新的监督QE模型相媲美。

原文标题：Unsupervised Quality Estimation for Neural Machine Translation

原文：Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time. Existing approaches require large amounts of expert annotated data, computation and time for training. As an alternative, we devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. Different from most of the current work that treats the MT system as a black box, we explore useful information that can be extracted from the MT system as a by-product of translation. By employing methods for uncertainty quantification, we achieve very good correlation with human judgments of quality, rivalling state-of-the-art supervised QE models. To evaluate our approach we collect the first dataset that enables work on both black-box and glass-box approaches to QE.

原文作者：Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, Lucia Specia

原文地址：https://arxiv.org/abs/2005.10608