Stochastic language generation in dialogue using recurrent neural networks with convolutional sentence reranking

The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add sig-nificantly to development costs and make cross-domain, multi-lingual dialogue sys-tems intractable. Moreover, human lan-guages are context-aware. The most nat-ural response should be directly learned from data rather than depending on pre-defined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolu-tional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or pre-defined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experi-mental conditions. Results of an evalua-tion by human judges indicate that it pro-duces not only high quality but linguisti-cally varied utterances which are preferred compared to n-gram and rule-based sys-tems.