Attention-based Neural Machine Translation Xxxxxxxx et al Sample Clauses

Attention-based Neural Machine Translation Xxxxxxxx et al. [2015] first introduce the attentional mechan- ism into neural machine translation to enable the decoder to focus on relevant parts of the source sentence during decod- ing. The attention mechanism allows a neural model to cope better with long sentences because it does not need to encode all the information of a source sentence into a fixed-length vector regardless of its length. In addition, the attentional mechanism allows us to look into the “black box” to gain in- sights on how NMT works from a linguistic perspective. Xxxxx et al. [2015a] propose two simple and effec- tive attentional mechanisms for neural machine translation and compare various alignment functions. They show that attention-based NMT are superior to non-attentional models in translating names and long sentences. cific network architectures, it is possible to apply it to the models proposed by Xxxxx et al. [2015a].