  Neural approaches have achieved state-of-the-art accuracy on machine
translation but suffer from the high cost of collecting large scale parallel
data. Thus, a lot of research has been conducted for neural machine translation
(NMT) with very limited parallel data, i.e., the low-resource setting. In this
paper, we provide a survey for low-resource NMT and classify related works into
three categories according to the auxiliary data they used: (1) exploiting
monolingual data of source and/or target languages, (2) exploiting data from
auxiliary languages, and (3) exploiting multi-modal data. We hope that our
survey can help researchers to better understand this field and inspire them to
design better algorithms, and help industry practitioners to choose appropriate
algorithms for their applications.
