Supervised Attentions For Neural Machine Translation
Haitao Mi, Zhiguo Wang, Abe Ittycheriah . Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing 2016 – 129 citations
In this paper, we improve the attention or alignment accuracy of neural machine translation by utilizing the alignments of training sentence pairs. We simply compute the distance between the machine attentions and the “true” alignments, and minimize this cost in the training procedure. Our experiments on large-scale Chinese-to-English task show that our model improves both translation and alignment qualities significantly over the large-vocabulary neural machine translation system, and even beats a state-of-the-art traditional syntax-based system.