  Generally, Neural Machine Translation models generate target words in a
left-to-right (L2R) manner and fail to exploit any future (right) semantics
information, which usually produces an unbalanced translation. Recent works
attempt to utilize the right-to-left (R2L) decoder in bidirectional decoding to
alleviate this problem. In this paper, we propose a novel \textbf{D}ynamic
\textbf{I}nteraction \textbf{M}odule (\textbf{DIM}) to dynamically exploit
target semantics from R2L translation for enhancing the L2R translation
quality. Different from other bidirectional decoding approaches, DIM firstly
extracts helpful target information through addressing and reading operations,
then updates target semantics for tracking the interactive history.
Additionally, we further introduce an \textbf{agreement regularization} term
into the training objective to narrow the gap between L2R and R2L translations.
Experimental results on NIST Chinese$\Rightarrow$English and WMT'16
English$\Rightarrow$Romanian translation tasks show that our system achieves
significant improvements over baseline systems, which also reaches comparable
results compared to the state-of-the-art Transformer model with much fewer
parameters of it.
