  In this paper, we propose a novel finetuning algorithm for the recently
introduced multi-way, mulitlingual neural machine translate that enables
zero-resource machine translation. When used together with novel many-to-one
translation strategies, we empirically show that this finetuning algorithm
allows the multi-way, multilingual model to translate a zero-resource language
pair (1) as well as a single-pair neural translation model trained with up to
1M direct parallel sentences of the same language pair and (2) better than
pivot-based translation strategy, while keeping only one additional copy of
attention-related parameters.
