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

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.

References in corpus (6)

• Adam: A Method for Stochastic Optimization
• Sequence to Sequence Learning with Neural Networks
• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Vocabulary Selection Strategies for Neural Machine Translation
• Neural Machine Translation with Recurrent Attention Modeling
• A Cheap Linear Attention Mechanism with Fast Lookups and Fixed-Size Representations