Stylistic Retrieval-based Dialogue System with Unparallel Training Data

TL;DR

This paper presents a novel framework for creating stylistic dialogue systems without parallel data by automatically generating stylized data and rewriting conversations, improving relevance, style, and user satisfaction.

Contribution

The study introduces a flexible, data augmentation-based approach to adapt retrieval-based dialogue systems to various styles without needing parallel datasets.

Findings

Outperforms baselines in relevance, style degree, and content diversity

Significantly improves user satisfaction in real-world testing

Effective across five distinct language styles

Abstract

The ability of a dialog system to express consistent language style during conversations has a direct, positive impact on its usability and on user satisfaction. Although previous studies have demonstrated that style transfer is feasible with a large amount of parallel data, it is often impossible to collect such data for different styles. In this paper, instead of manually constructing conversation data with a certain style, we propose a flexible framework that adapts a generic retrieval-based dialogue system to mimic the language style of a specified persona without any parallel data. Our approach is based on automatic generation of stylized data by learning the usage of jargon, and then rewriting the generic conversations to a stylized one by incorporating the jargon. In experiments we implemented dialogue systems with five distinct language styles, and the result shows our framework significantly outperforms baselines in terms of the average score of responses' relevance and style degree, and content diversity. A/B testing on a commercial chatbot shows that users are more satisfied with our system. This study demonstrates the feasibility of building stylistic dialogue systems by simple data augmentation.