Baselines We thoroughly compare our approaches to the following baselines:

Direct source$\rightarrow $target: A standard NMT model trained on given source$\rightarrow $target parallel data.

Multilingual: A single, shared NMT model for multiple translation directions BIBREF6.

Many-to-many: Trained for all possible directions among source, target, and pivot languages.

Many-to-one: Trained for only the directions to target language, i.e., source$\rightarrow $target and pivot$\rightarrow $target, which tends to work better than many-to-many systems BIBREF27. 
 Question: What are multilingual models that were outperformed in performed experiment?
Direct source$\rightarrow $target: A standard NMT model trained on given source$\rightarrow $target Multilingual: A single, shared NMT model for multiple translation directions Many-to-many: Trained for all possible directions among source, target, and pivot languages Many-to-one: Trained for only the directions to target language