论文标题
无监督的神经机器翻译质量估计
Unsupervised Quality Estimation for Neural Machine Translation
论文作者
论文摘要
质量估计（QE）是使机器翻译（MT）在现实世界应用中有用的重要组成部分，因为它旨在告知用户测试时MT输出的质量。现有方法需要大量的专家注释数据，计算和培训时间。作为替代方案，我们设计了一种无监督的量化宽松方法，除了MT系统本身之外，没有培训或访问其他资源。与当前将MT系统视为黑匣子的大多数工作不同，我们探索了可以从MT系统中提取的有用信息，作为翻译的副产品。通过采用不确定性量化的方法，我们与人类质量判断，具有与众不同的最新监督量化宽松模型实现了很好的相关性。为了评估我们的方法，我们收集了第一个数据集，该数据集可以在黑框和玻璃盒方法上使用量化宽松的方法。
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.
