The Design and Implementation of XiaoIce, an Empathetic Social Chatbot

The Design and Implementation of XiaoIce, an Empathetic Social Chatbot Li Zhou Microsoft lzhou@microsoft.com Jianfeng Gao Microsoft Research jfgao@microsoft.com Di Li Microsoft lidi@microsoft.com Heung-Yeung Shum Microsoft hshum@microsoft.com This article describes the development of Microsoft XiaoIce, the most popular social chatbot in the world. XiaoIce is uniquely designed as an artifical intelligence companion with an emotional connection to satisfy the human need for communication, affection, and social belonging. We take into account both intelligent quotient and emotional quotient in system design, cast human– machine social chat as decision-making over Markov Decision Processes, and optimize XiaoIce for long-term user engagement, measured in expected Conversation-turns Per Session (CPS). We detail the system architecture and key components, including dialogue manager, core chat, skills, and an empathetic computing module. We show how XiaoIce dynamically recognizes human feelings and states, understands user intent, and responds to user needs throughout long conversations. Since the release in 2014, XiaoIce has communicated with over 660 million active users and succeeded in establishing long-term relationships with many of them. Analysis of large- scale online logs shows that XiaoIce has achieved an average CPS of 23, which is significantly higher than that of other chatbots and even human conversations. 1. Introduction The development of social chatbots, or intelligent dialogue systems that are able to engage in empathetic conversations with humans, has been one of the longest running goals in artificial intelligence (AI). Early conversational systems, such as Eliza (Weizenbaum 1966), Parry (Colby, Weber, and Hilf 1971), and Alice (Wallace 2009), were Submission received: 17 January 2019; revised version received: 15 September 2019; accepted for publication: 17 November 2019. https://doi.org/10.1162/COLI_a_00368 © 2020 Association for Computational Linguistics Published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license Computational Linguistics Volume 46, Number 1 designed to mimic human behavior in a text-based conversation, hence to pass the Turing Test within a controlled scope. Despite impressive successes, these systems were mostly based on hand-crafted rules and worked well only in constrained environments. An open-domain social chatbot had remained an elusive goal until recently. Lately, we have been witnessing promising results in both the academic research community and industry as large volumes of conversational data become available, and breakthroughs in machine learning are applied to conversational AI. Recent surveys include Gao, Galley, and Li (2019) and Shum, He, and Li (2018). In this article we present the design and implementation of Microsoft XiaoIce (‘Little Ice’ literally in Chinese), the most popular social chatbot in the world. Since her launch in China in May 2014, XiaoIce has attracted over 660 million active users (i.e., subscribed users). XiaoIce has already been shipped in five countries (China, Japan, US, India, and Indonesia) under different names (e.g., Rinna in Japan) on more than 40 platforms, including WeChat, QQ, Weibo, and Meipai in China; Facebook Messenger in the United States and India; and LINE in Japan and Indonesia. The primary design goal of XiaoIce is to be an AI companion with which users form long-term, emotional connections. Being able …

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