Stylized Dialogue Response Generation Using Stylized Unpaired Texts

by   Yinhe Zheng, et al.

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



There are no comments yet.


page 1

page 2

page 3

page 4


A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data

Endowing dialogue systems with personas is essential to deliver more hum...

Fact-based Dialogue Generation with Convergent and Divergent Decoding

Fact-based dialogue generation is a task of generating a human-like resp...

Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems

Open-domain dialogue systems aim to generate relevant, informative and e...

"Nice Try, Kiddo": Ad Hominems in Dialogue Systems

Ad hominem attacks are those that attack some feature of a person's char...

Stylistic Dialogue Generation via Information-Guided Reinforcement Learning Strategy

Stylistic response generation is crucial for building an engaging dialog...

Prototype-to-Style: Dialogue Generation with Style-Aware Editing on Retrieval Memory

The ability of a dialog system to express prespecified language style du...

Towards Efficiently Diversifying Dialogue Generation via Embedding Augmentation

Dialogue generation models face the challenge of producing generic and r...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.