Neural Machine Translation with Bilingual History Involved Attention

Abstract

The using of attention in neural machine translation (NMT) has greatly improved translation performance, but NMT models usually calculate attention vectors independently at different time steps and consequently suffer from over-translation and under-translation. To mitigate the problem, in this paper we propose a method to consider the translated source and target information up to now related to each source word when calculating attentions. The main idea is to keep track of the translated source and target information assigned to each source word at each time step and then accumulate these information to get the completion degree for each source word. In this way, in the later calculation of the attention, the model can adjust the attention weights to give a reasonable final completion degree for each source word. Experimental results show that our method can outperform the strong baseline systems significantly both on the Chinese-English and English-German translation tasks and produce better alignment on the human aligned data set.