  The principal task in supervised neural machine translation (NMT) is to learn
to generate target sentences conditioned on the source inputs from a set of
parallel sentence pairs, and thus produce a model capable of generalizing to
unseen instances. However, it is commonly observed that the generalization
performance of the model is highly influenced by the amount of parallel data
used in training. Although data augmentation is widely used to enrich the
training data, conventional methods with discrete manipulations fail to
generate diverse and faithful training samples. In this paper, we present a
novel data augmentation paradigm termed Continuous Semantic Augmentation
(CsaNMT), which augments each training instance with an adjacency semantic
region that could cover adequate variants of literal expression under the same
meaning. We conduct extensive experiments on both rich-resource and
low-resource settings involving various language pairs, including WMT14
English-{German,French}, NIST Chinese-English and multiple low-resource IWSLT
translation tasks. The provided empirical evidences show that CsaNMT sets a new
level of performance among existing augmentation techniques, improving on the
state-of-the-art by a large margin. The core codes are contained in Appendix E.
