Stylized Dialogue Generation with Feature-Guided Knowledge Augmentation (2023)

Abstract: Stylized dialogue generation systems aim to produce coherent and context-aware dialogues while effectively emulating the desired style. Generating stylized dialogue is valuable yet challenging due to the scarce parallel data. Existing methods often synthesize pseudo data through back translation, yet suffer from noisy and context-agnostic style signals caused by insufficient guidance on target style features. To address this, we propose the knowledge-augmented stylized dialogue generation model, which includes a feature-guided style knowledge selection module that utilizes context and response features. Specifically, we retrieve dialogue-related style sentences from style corpus to explicitly provide clear style signals. We design a feature-guided selection module with response-related contrastive learning and style responsiveness Kullback-Leibler losses to enhance generation at both semantic and stylized levels. Our approach demonstrates satisfactory performance on two public stylized dialogue benchmarks in both automatic and human evaluations.

## introduction with the increasing demand for meaningful humancomputer interaction, the development of advanced dialogue systems has become crucial across various domains. stylized dialogue generation focuses on producing human-like conversations by generating stylized and coherent responses. to achieve this, significant efforts have been dedicated to building response-matching models that leverage both dialogue content and style sentences, resulting in more engaging and diverse responses @xcite @xcite . ## conclusion in this paper, we propose a knowledge-augmented stylized dialogue generation model with a featureguided selection module to provide better guidance signals from both semantic and stylistic perspectives. specifically, we exploit the style knowledge from a style corpus to provide guiding style signal and design a response-related contrastive learning and a style responsiveness kullback-leibler loss to enhance the semantic and stylistic features. experiments on two public benchmark datasets demonstrate that our proposed method has satisfactory results in generating response with desired style in four target styles. in the future, we plan to explore model performance in zero-shot scenarios and to combine the ability of large language models for intelligent dialogue systems.