A Simple and Effective Approach to Coverage-Aware Neural Machine Translation
Yanyang Li, Tong Xiao, Yinqiao Li, Qiang Wang, Changming Xu, Jingbo Zhu · 2018
We offer a simple and effective method to seek a better balance between model confidence and length preference for Neural Machine Translation (NMT).Unlike the popular length normalization and coverage models, our model does not require training nor reranking the limited n-best outputs.Moreover, it is robust to large beam sizes, which is not well studied in previous work.On the Chinese-English and English-German translation tasks, our approach yields +0.4 ∼ 1.5 BLEU improvements over the state-of-the-art baselines.