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 significantly to development
costs and make cross-domain, multi-lingual dialogue systems intractable.
Moreover, human languages are context-aware. The most natural response should
be directly learned from data rather than depending on predefined syntaxes or
rules. This paper presents a statistical language generator based on a joint
recurrent and convolutional neural network structure which can be trained on
dialogue act-utterance pairs without any semantic alignments or predefined
grammar trees. Objective metrics suggest that this new model outperforms
previous methods under the same experimental conditions. Results of an
evaluation by human judges indicate that it produces not only high quality but
linguistically varied utterances which are preferred compared to n-gram and
rule-based systems.

本文提出基于联合循环卷积神经网络结构的统计语言生成器，可以在不需要语义对齐或预定义语法树的情况下，训练对话行为 - 话语对。客观指标表明，在相同的实验条件下，这种新模型优于以前的方法。人类评委的评估结果表明，它产生的不仅是高质量而且是语言上多变的话语，而且与 n-gram 和基于规则的系统相比受到更多的青睐。