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Most chatbot literature that focuses on improving the fluency and coherenceof a chatbot, is dedicated to making chatbots more human-like. However, verylittle work delves into what really separates humans from chatbots -- humansintrinsically understand the effect their responses have on the interlocutorand often respond with an intention such as proposing an optimistic view tomake the interlocutor feel better. This paper proposes an innovative frameworkto train chatbots to possess human-like intentions. Our framework includes aguiding chatbot and an interlocutor model that plays the role of humans. Theguiding chatbot is assigned an intention and learns to induce the interlocutorto reply with responses matching the intention, for example, long responses,joyful responses, responses with specific words, etc. We examined our frameworkusing three experimental setups and evaluated the guiding chatbot with fourdifferent metrics to demonstrate flexibility and performance advantages.Additionally, we performed trials with human interlocutors to substantiate theguiding chatbot's effectiveness in influencing the responses of humans to acertain extent. Code will be made available to the public.

Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn Chatbot Responding with Intention