Attention-based sequence-to-sequence model has proved successful in Neural Machine Translation (NMT). However, the attention without consideration of decoding history, which includes the past information in the decoder and the attention mechanism, often causes much repetition. To address this problem, we propose the decoding-history-based Adaptive Control of Attention (ACA) for the NMT model. ACA learns to control the attention by keeping track of the decoding history and the current information with a memory vector, so that the model can take the translated contents and the current information into consideration. Experiments on Chinese-English translation and the English-Vietnamese translation have demonstrated that our model significantly outperforms the strong baselines. The analysis shows that our model is capable of generating translation with less repetition and higher accuracy. The code will be available at https://github.com/lancopku

提出了一种基于解码历史的自适应注意力控制机制，可以解决注意力在缺乏解码历史信息的情况下导致频繁重复的问题，在中英文和英越翻译任务上均表现出较高的翻译准确性，可生成较少重复的翻译。

基于解码历史的自适应控制注意力的神经机器翻译