  In this paper, we present an extensive investigation of multi-bridge,
many-to-many multilingual NMT models (MB-M2M) ie., models trained on
non-English language pairs in addition to English-centric language pairs. In
addition to validating previous work which shows that MB-M2M models can
overcome zeroshot translation problems, our analysis reveals the following
results about multibridge models: (1) it is possible to extract a reasonable
amount of parallel corpora between non-English languages for low-resource
languages (2) with limited non-English centric data, MB-M2M models are
competitive with or outperform pivot models, (3) MB-M2M models can outperform
English-Any models and perform at par with Any-English models, so a single
multilingual NMT system can serve all translation directions.
