  The success of Neural Machine Translation (NMT) largely depends on the
availability of large bitext training corpora. Due to the lack of such large
corpora in low-resource language pairs, NMT systems often exhibit poor
performance. Extra relevant monolingual data often helps, but acquiring it
could be quite expensive, especially for low-resource languages. Moreover,
domain mismatch between bitext (train/test) and monolingual data might degrade
the performance. To alleviate such issues, we propose AUGVIC, a novel data
augmentation framework for low-resource NMT which exploits the vicinal samples
of the given bitext without using any extra monolingual data explicitly. It can
diversify the in-domain bitext data with finer level control. Through extensive
experiments on four low-resource language pairs comprising data from different
domains, we have shown that our method is comparable to the traditional
back-translation that uses extra in-domain monolingual data. When we combine
the synthetic parallel data generated from AUGVIC with the ones from the extra
monolingual data, we achieve further improvements. We show that AUGVIC helps to
attenuate the discrepancies between relevant and distant-domain monolingual
data in traditional back-translation. To understand the contributions of
different components of AUGVIC, we perform an in-depth framework analysis.
