One of the biggest challenges of end-to-end language generation from meaning
representations in dialogue systems is making the outputs more natural and
varied. Here we take a large corpus of 50K crowd-sourced utterances in the
restaurant domain and develop text analysis methods that systematically
characterize types of sentences in the training data. We then automatically
label the training data to allow us to conduct two kinds of experiments with a
neural generator. First, we test the effect of training the system with
different stylistic partitions and quantify the effect of smaller, but more
stylistically controlled training data. Second, we propose a method of labeling
the style variants during training, and show that we can modify the style of
the generated utterances using our stylistic labels. We contrast and compare
these methods that can be used with any existing large corpus, showing how they
vary in terms of semantic quality and stylistic control.

使用文本分析方法，基于餐饮行业中的 50K 句子，对对话系统中从意义表示到生成自然对话的进行了探究，使用不同的风格分区来训练和生成自然对话，并证明了标记风格变量的方法可以调整生成对话的风格。