Abstract
The quality of Neural Machine Translation (NMT) has been shown to significantly degrade when confronted with source-side noise. We present the first large-scale study of state-of-the-art English-to-German NMT on real grammatical noise, by evaluating on several Grammar Correction corpora. We present methods for evaluating NMT robustness without true references, and we use them for extensive analysis of the effects that different grammatical errors have on the NMT output. We also introduce a technique for visualizing the divergence distribution caused by a source-side error, which allows for additional insights.
Abstract (translated)
神经源机器翻译（NMT）的质量已被证明在面对源侧噪声时显著降低。本文通过对几种语法纠正语料库的评价，首次对德文非母语教学中的真正语法噪声进行了大规模的研究。我们提出了无真实参考的NMT鲁棒性评估方法，并将其用于广泛分析不同语法错误对NMT输出的影响。我们还介绍了一种可视化由源端错误引起的散度分布的技术，它允许更多的洞察。
URL
https://arxiv.org/abs/1905.10024
