  To improve the performance of Neural Machine Translation~(NMT) for
low-resource languages~(LRL), one effective strategy is to leverage parallel
data from a related high-resource language~(HRL). However, multilingual data
has been found more beneficial for NMT models that translate from the LRL to a
target language than the ones that translate into the LRLs. In this paper, we
aim to improve the effectiveness of multilingual transfer for NMT models that
translate \emph{into} the LRL, by designing a better decoder word embedding.
Extending upon a general-purpose multilingual encoding method Soft Decoupled
Encoding~\citep{SDE}, we propose DecSDE, an efficient character n-gram based
embedding specifically designed for the NMT decoder. Our experiments show that
DecSDE leads to consistent gains of up to 1.8 BLEU on translation from English
to four different languages.
