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

• URL: http://arxiv.org/abs/2103.16429v3
• Date: Thu, 1 Apr 2021 03:42:16 GMT
• Title: Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn Chatbot Responding with Intention
• Authors: Hsuan Su, Jiun-Hao Jhan, Fan-yun Sun, Saurav Sahay, Hung-yi Lee
• Abstract summary: This paper proposes an innovative framework to train chatbots to possess human-like intentions. Our framework included a guiding robot and an interlocutor model that plays the role of humans. We examined our framework using three experimental setups and evaluate the guiding robot with four different metrics to demonstrated flexibility and performance advantages.
• Score: 55.77218465471519
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 included a guiding chatbot and an interlocutor model that plays the role of humans. The guiding chatbot was assigned an intention and learned 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 evaluate the guiding chatbot with four different metrics to demonstrated 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.

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