  We study several methods for full or partial sharing of the decoder
parameters of multilingual NMT models. We evaluate both fully supervised and
zero-shot translation performance in 110 unique translation directions using
only the WMT 2019 shared task parallel datasets for training. We use additional
test sets and re-purpose evaluation methods recently used for unsupervised MT
in order to evaluate zero-shot translation performance for language pairs where
no gold-standard parallel data is available. To our knowledge, this is the
largest evaluation of multi-lingual translation yet conducted in terms of the
total size of the training data we use, and in terms of the diversity of
zero-shot translation pairs we evaluate. We conduct an in-depth evaluation of
the translation performance of different models, highlighting the trade-offs
between methods of sharing decoder parameters. We find that models which have
task-specific decoder parameters outperform models where decoder parameters are
fully shared across all tasks.
