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The principal task in supervised neural machine translation (NMT) is to learnto generate target sentences conditioned on the source inputs from a set ofparallel sentence pairs, and thus produce a model capable of generalizing tounseen instances. However, it is commonly observed that the generalizationperformance of the model is highly influenced by the amount of parallel dataused in training. Although data augmentation is widely used to enrich thetraining data, conventional methods with discrete manipulations fail togenerate diverse and faithful training samples. In this paper, we present anovel data augmentation paradigm termed Continuous Semantic Augmentation(CsaNMT), which augments each training instance with an adjacency semanticregion that could cover adequate variants of literal expression under the samemeaning. We conduct extensive experiments on both rich-resource andlow-resource settings involving various language pairs, including WMT14English-{German,French}, NIST Chinese-English and multiple low-resource IWSLTtranslation tasks. The provided empirical evidences show that CsaNMT sets a newlevel of performance among existing augmentation techniques, improving on thestate-of-the-art by a large margin. The core codes are contained in Appendix E.

Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation