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Recently, the attention mechanism plays a key role to achieve highperformance for Neural Machine Translation models. However, as it computes ascore function for the encoder states in all positions at each decoding step,the attention model greatly increases the computational complexity. In thispaper, we investigate the adequate vision span of attention models in thecontext of machine translation, by proposing a novel attention framework thatis capable of reducing redundant score computation dynamically. The term"vision span" means a window of the encoder states considered by the attentionmodel in one step. In our experiments, we found that the average window size ofvision span can be reduced by over 50% with modest loss in accuracy onEnglish-Japanese and German-English translation tasks.% This results indicatethat the conventional attention mechanism performs a significant amount ofredundant computation.

An Empirical Study of Adequate Vision Span for Attention-Based Neural Machine Translation