Current Knowledge-Grounded Dialogue Generation (KDG) models specialize in
producing rational and factual responses. However, to establish long-term
relationships with users, the KDG model needs the capability to generate
responses in a desired style or attribute. Thus, we study a new problem:
Stylized Knowledge-Grounded Dialogue Generation (SKDG). It presents two
challenges: (1) How to train a SKDG model where no <context, knowledge,
stylized response> triples are available. (2) How to cohere with context and
preserve the knowledge when generating a stylized response. In this paper, we
propose a novel disentangled template rewriting (DTR) method which generates
responses via combing disentangled style templates (from monolingual stylized
corpus) and content templates (from KDG corpus). The entire framework is
end-to-end differentiable and learned without supervision. Extensive
experiments on two benchmarks indicate that DTR achieves a significant
improvement on all evaluation metrics compared with previous state-of-the-art
stylized dialogue generation methods. Besides, DTR achieves comparable
performance with the state-of-the-art KDG methods in standard KDG evaluation
setting.

本研究提出了一种基于 knowledge-grounded 的对话生成模型，称为 Disentangled Template Rewriting（DTR）模型，它能够生成符合预期风格的对话，且整个框架是可学习的，并在两个基准测试中得到了显著改进的结果。

基于知识的对话生成：解耦模板重写

Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting

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.

本研究提出一种基于逆向对话模型和风格路由方法的对话生成技术，能够捕捉嵌入在不成对文本中的风格特征，产生既符合给定上下文又符合目标风格的对话回复，实验表明方法能够胜过竞争性对话系统在产生连贯、风格强烈的对话回复方面的表现。