Abstract
The training objective of neural machine
translation (NMT) is to minimize the loss
between the words in the translated sentences
and those in the references. In NMT, there is
a natural correspondence between the source
sentence and the target sentence. However,
this relationship has only been represented
using the entire neural network and the
training objective is computed in wordlevel. In this paper, we propose a sentencelevel agreement module to directly minimize
the difference between the representation of
source and target sentence. The proposed
agreement module can be integrated into NMT
as an additional training objective function and
can also be used to enhance the representation
of the source sentences. Empirical results
on the NIST Chinese-to-English and WMT
English-to-German tasks show the proposed
agreement module can significantly improve
the NMT performance