SalesBot 2.0: A Human-Like Intent-Guided Chit-Chat Dataset

TL;DR

SalesBot 2.0 introduces an improved, large-scale dialogue dataset that enhances naturalness and consistency in chit-chat to task-oriented conversations using large language models.

Contribution

This work presents a revised dataset leveraging LLMs for better naturalness and consistency in intent-guided dialogues, addressing limitations of previous BlenderBot-based data.

Findings

Dataset exhibits smoother topic transitions

Dialogues are more human-like in naturalness and consistency

Framework enables generation of diverse target intents

Abstract

In recent research on dialogue systems and corpora, there has been a significant focus on two distinct categories: task-oriented (TOD) and open-domain (chit-chat) dialogues. TOD systems aim to satisfy specific user goals, such as finding a movie to watch, whereas open-domain systems primarily focus on generating engaging conversations. A recent study by Chiu et al. (2022) introduced SalesBot, which provides simulators and a dataset with one-turn transition from chit-chat to task-oriented dialogues. However, the previously generated data solely relied on BlenderBot, which raised concerns about its long-turn naturalness and consistency during a conversation. To address this issue, this paper aims to build SalesBot 2.0, a revised version of the published data, by leveraging the commonsense knowledge of large language models (LLMs) through proper prompting. The objective is to gradually bridge the gap between chit-chat and TOD towards better naturalness and consistency. The newly released large-scale dataset with detailed annotations exhibits smoother transitions between topics and is more human-like in terms of naturalness and consistency. It can serve as a valuable resource for both academic research and commercial applications. Furthermore, our proposed framework can be applied to generate numerous dialogues with various target intents.