Stylized Dialogue Response Generation Using Stylized Unpaired Texts

Generating stylized responses is essential to build intelligent and engaging dialogue systems. However, this task far from well-explored due the difficulties of rendering a particular style in coherent responses, especially when target embedded only unpaired texts that cannot be directly used train model. This paper proposes generation method can capture stylistic features texts. Specifically, our produce are both given context conform style. In study, an inverse model first introduced predict possible posts for input responses. Then generate pseudo pairs based on these Further, employed with joint training process. A routing approach proposed intensify decoder. Automatic manual evaluations two datasets demonstrate outperforms competitive baselines producing style-intensive