将Chatbot放入对话者的鞋子：学习Chatbot响应意图的新框架
Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn Chatbot Responding with Intention
摘要：
大多数专注于提高聊天机器人流畅性和连贯性的聊天机器人文献都致力于使聊天机器人更像人类。然而，很少有工作深入研究人类与聊天机器人的真正区别-人类本质上理解他们的反应对对话者的影响，并且经常做出回应，例如提出乐观的观点，让对话者感觉更好。本文提出了一个创新的框架，训练聊天机器人具有类似人类的意图。我们的框架包括一个指导聊天机器人和一个扮演人类角色的对话者模型。指导聊天机器人被分配一个意图，并学会诱导对话者回复与意图相匹配的响应，例如，长响应，快乐的响应，特定词语的响应等。我们使用三个实验设置检查了我们的框架，并评估了指导聊天机器人与四个不同的指标，以展示灵活性和性能优势。此外，我们与人类对话者进行了试验，以证实引导聊天机器人在一定程度上影响人类反应的有效性。代码将向公众开放。
英文摘要：
Most chatbot literature that 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 examined our framework using three experimental setups and evaluated the guiding chatbot with four different metrics to demonstrate flexibility and performance advantages. Additionally, we performed trials with human interlocutors to substantiate the guiding chatbot's effectiveness in influencing the responses of humans to a certain extent. Code will be made available to the public.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
