Most chatbot literature focuses on improving the fluency and coherence of a chatbot, is dedicated to making chatbots more human-like. However, very little work delves into what really separates humans from chatbots -- humans intrinsically understand the effect their responses have on the interlocutor and often respond with an intention such as proposing an optimistic view to make the interlocutor feel better. This paper proposes an innovative framework to train chatbots to possess human-like intentions. Our framework includes a guiding chatbot and an interlocutor model that plays the role of humans. The guiding chatbot is assigned an intention and learns to induce the interlocutor to reply with responses matching the intention, for example, long responses, joyful responses, responses with specific words, etc. We examine our framework using three experimental setups and evaluate the guiding chatbot with four different metrics to demonstrate flexibility and performance advantages. Additionally, human evaluation results sufficiently substantiate the guiding chatbot's effectiveness in influencing humans' responses to a certain extent. Code will be made available to the public.

本文提出了一种基于指导聊天机器人学习人类回应意图的创新框架。使用三个实验设置对其进行了评估并利用四种不同的度量标准评估其灵活性和性能优势。此外，我们还通过人际合作试验证明了指导聊天机器人在一定程度上影响人类回应的有效性。

让聊天机器人置身其对话者角度：新的框架学习具备意图的聊天机器人回应技巧