Natural language generation plays a critical role in spoken dialogue systems.
We present a new approach to natural language generation for task-oriented
dialogue using recurrent neural networks in an encoder-decoder framework. In
contrast to previous work, our model uses both lexicalized and delexicalized
components i.e. slot-value pairs for dialogue acts, with slots and
corresponding values aligned together. This allows our model to learn from all
available data including the slot-value pairing, rather than being restricted
to delexicalized slots. We show that this helps our model generate more natural
sentences with better grammar. We further improve our model's performance by
transferring weights learnt from a pretrained sentence auto-encoder. Human
evaluation of our best-performing model indicates that it generates sentences
which users find more appealing.

本研究提出了一种新的自然语言生成方法，使用循环神经网络进行任务导向对话的自然语言生成，它通过对齐词槽和相应值的方法，同时使用词汇化和非词汇化的组件，学习所有可用的数据，包括词槽价值对，从而生成更自然、语法更好的句子；通过迁移预训练的句子编码器中学到的权重，进一步提高了模型的性能，并获得人类评价良好的表现。