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Effective Approaches to Attention-based Neural Machine ...

Effective Approaches to Attention-based Neural Machine Translation Minh-Thang Luong Hieu Pham Christopher D. Manning Computer Science Department, Stanford University, Stanford, CA 94305. Abstract X Y Z <eos>. An attentional mechanism has lately been used to improve Neural Machine transla- tion (NMT) by selectively focusing on parts of the source sentence during trans- lation. However, there has been little work exploring useful architectures for Attention-based NMT. This paper exam- A B C D <eos> X Y Z. ines two simple and Effective classes of at- tentional mechanism: a global approach Figure 1: Neural Machine translation a stack- which always attends to all source words ing recurrent architecture for translating a source and a local one that only looks at a subset sequence A B C D into a target sequence X Y. of source words at a time.

lowing models to learn alignments between dif-ferent modalities, e.g., between image objects and agent actions in the dynamic control problem (Mnih et al., 2014), between speech frames and text in the speech recognition task (Chorowski et al., 2014), or between visual features of a picture and its text description in the image caption gen-

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