Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems
Bo-Hsiang Tseng, Florian Kreyssig, Paweł Budzianowski, Iñigo Casanueva, Yen-Chen Wu, Stefan Ultes, Milica Gašić · 2018
Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling.Given a semantic representation provided by the dialogue manager, the language generator should generate sentences that convey desired information.Traditional template-based generators can produce sentences with all necessary information, but these sentences are not sufficiently diverse.With RNN-based models, the diversity of the generated sentences can be high, however, in the process some information is lost.In this work, we improve an RNN-based generator by considering latent information at the sentence level during generation using the conditional variational autoencoder architecture.We demonstrate that our model outperforms the original RNN-based generator, while yielding highly diverse sentences.In addition, our model performs better when the training data is limited.