Abstract
The reordering model plays an important role
in phrase-based statistical machine translation.
However, there are few works that exploit
the reordering information in neural machine
translation. In this paper, we propose a
reordering mechanism to learn the reordering
embedding of a word based on its contextual
information. These reordering embeddings are
stacked together with self-attention networks
to learn sentence representation for machine
translation. The reordering mechanism can be
easily integrated into both the encoder and the
decoder in the Transformer translation system.
Experimental results on WMT’14 English-toGerman, NIST Chinese-to-English, and WAT
ASPEC Japanese-to-English translation tasks
demonstrate that the proposed methods can
significantly improve the performance of the
Transformer translation system.