We began with evaluating standard MT paradigms, i.e., PBSMT BIBREF3 and NMT BIBREF1 . As for PBSMT, we also examined two advanced methods: pivot-based translation relying on a helping language BIBREF10 and induction of phrase tables from monolingual data BIBREF14 .

As for NMT, we compared two types of encoder-decoder architectures: attentional RNN-based model (RNMT) BIBREF2 and the Transformer model BIBREF18 . In addition to standard uni-directional modeling, to cope with the low-resource problem, we examined two multi-directional models: bi-directional model BIBREF11 and multi-to-multi (M2M) model BIBREF8 .

After identifying the best model, we also examined the usefulness of a data augmentation method based on back-translation BIBREF17 . 
 Question: what was the baseline?
pivot-based translation relying on a helping language BIBREF10 nduction of phrase tables from monolingual data BIBREF14  attentional RNN-based model (RNMT) BIBREF2 Transformer model BIBREF18 bi-directional model BIBREF11 multi-to-multi (M2M) model BIBREF8 back-translation BIBREF17