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Recent deep learning models have shown improving results to natural languagegeneration (NLG) irrespective of providing sufficient annotated data. However,a modest training data may harm such models performance. Thus, how to build agenerator that can utilize as much of knowledge from a low-resource settingdata is a crucial issue in NLG. This paper presents a variational neural-basedgeneration model to tackle the NLG problem of having limited labeled dataset,in which we integrate a variational inference into an encoder-decoder generatorand introduce a novel auxiliary autoencoding with an effective trainingprocedure. Experiments showed that the proposed methods not only outperform theprevious models when having sufficient training dataset but also show strongability to work acceptably well when the training data is scarce.

Dual Latent Variable Model for Low-Resource Natural Language Generation in Dialogue Systems