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- W2952238405 abstract "Recently, the attention mechanism plays a key role to achieve high performance for Neural Machine Translation models. However, as it computes a score function for the encoder states in all positions at each decoding step, the attention model greatly increases the computational complexity. In this paper, we investigate the adequate vision span of attention models in the context of machine translation, by proposing a novel attention framework that is capable of reducing redundant score computation dynamically. The term vision span means a window of the encoder states considered by the attention model in one step. In our experiments, we found that the average window size of vision span can be reduced by over 50% with modest loss in accuracy on English-Japanese and German-English translation tasks.% This results indicate that the conventional attention mechanism performs a significant amount of redundant computation." @default.
- W2952238405 created "2019-06-27" @default.
- W2952238405 creator A5050229964 @default.
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- W2952238405 date "2016-12-18" @default.
- W2952238405 modified "2023-09-26" @default.
- W2952238405 title "An Empirical Study of Adequate Vision Span for Attention-Based Neural Machine Translation" @default.
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- W2952238405 doi "https://doi.org/10.48550/arxiv.1612.06043" @default.
- W2952238405 hasPublicationYear "2016" @default.
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