  Multilingual training of neural machine translation (NMT) systems has led to
impressive accuracy improvements on low-resource languages. However, there are
still significant challenges in efficiently learning word representations in
the face of paucity of data. In this paper, we propose Soft Decoupled Encoding
(SDE), a multilingual lexicon encoding framework specifically designed to share
lexical-level information intelligently without requiring heuristic
preprocessing such as pre-segmenting the data. SDE represents a word by its
spelling through a character encoding, and its semantic meaning through a
latent embedding space shared by all languages. Experiments on a standard
dataset of four low-resource languages show consistent improvements over strong
multilingual NMT baselines, with gains of up to 2 BLEU on one of the tested
languages, achieving the new state-of-the-art on all four language pairs.
