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Unlike early chatbots designed for chitchat, XiaoIce is designed as a social chatbot intended to serve users’ needs for communication, affection, and social belonging, and is endowed with empathy, personality, and skills, integrating both emotional quotient and intelligence quoti...

The Design and Implementation of XiaoIce, an Empathetic Social Chatbot.

arXiv: Human-Computer Interaction, (2019)

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Abstract

This paper describes the development of the Microsoft XiaoIce system, the most popular social chatbot in the world. XiaoIce is uniquely designed as an AI companion with an emotional connection to satisfy the human need for communication, affection, and social belonging. We take into account both intelligent quotient (IQ) and emotional quo...More

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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).
  • Conversational systems, such as Eliza (Weizenbaum 1966), Parry (Colby, Weber, and Hilf 1971), and Alice (Wallace 2009), were.
  • Recent surveys include Gao, Galley, and Li (2019) and Shum, He, and Li (2018)
Highlights
  • 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)
  • General Chat is responsible for engaging in open-domain conversations that cover a wide range of topics
  • Because General Chat and Domain Chats are implemented using the same engine with access to different databases, we only describe General Chat here
  • The components of Image Commenting, including the text-to-image generator and boosted tree ranker, are trained on a data set consisting of 28 million images, each paired with six text comments rated on the three-level quality scale as shown in Figure 13
  • Evaluating the quality of open-domain social chatbots is challenging because social chats are inherently open-ended (Ram et al 2018; Gao, Galley, and Li 2019; Huang, Zhu, and Gao 2019) and the long-term success of a social chatbot needs to be measured by its user engagement
  • Unlike early chatbots designed for chitchat, XiaoIce is designed as a social chatbot intended to serve users’ needs for communication, affection, and social belonging, and is endowed with empathy, personality, and skills, integrating both emotional quotient and intelligence quotient to optimize for long-term user engagement, measured in expected Conversation-turns Per Session
Methods
  • Design Principle

    Social chatbots require a sufficiently high intelligence quotient (IQ) to acquire a range of skills to keep up with the users and help them complete specific tasks.
  • Importantly, social chatbots require a sufficient emotional quotient (EQ) to meet users’ emotional needs, such as emotional affection and social belonging, which are among the fundamental needs for human beings (Maslow 1943).
  • Integration of both IQ and EQ is core to XiaoIce’s system design.
  • The most important and sophisticated skill is Core Chat, which can engage in long and opendomain conversations with users
Results
  • Both the topic switching classifier and the topic ranker are trained using 50K dialogue sessions whose topics are manually labeled.
  • It can be observed that the XiaoIce-produced comments are emotional, subjective, imaginative, and are very likely to inspire meaningful human–machine interactions, while the comments generated by the other image captioning models are reasonable in content but boring in the context of social chats, and less likely to improve user engagement
  • Most of these skills are designed for very specific user scenarios or tasks, implemented using hand-crafted dialogue policies and template-based response generators unless otherwise stated.
  • A skill can be retired or reenter the market based on the market study result
Conclusion
  • 7.1 Evaluation Metrics

    Evaluating the quality of open-domain social chatbots is challenging because social chats are inherently open-ended (Ram et al 2018; Gao, Galley, and Li 2019; Huang, Zhu, and Gao 2019) and the long-term success of a social chatbot needs to be measured by its user engagement.
  • There is no doubt that the most reliable evaluation is to deploy the chatbot to users and monitor the user feedback and engagement, measured by user ratings, NAU, CPS, and so on, over a long period of time
  • The authors take this approach to evaluate XiaoIce. Some recent dialogue challenges (Dinan et al 2018; Ram et al 2018) take a similar, manual evaluation approach, using paid workers and unpaid volunteers.
  • The authors will continue to make XiaoIce more useful and empathetic to help build a more connected and happier society for all
Tables
  • Table1: Perplexity and BLEU for the seq2seq and persona models on the TV series data set. Adapted from <a class="ref-link" id="cLi_et+al_2016_b" href="#rLi_et+al_2016_b">Li et al (2016b</a>)
  • Table2: Responses to “Do you love me?” from the persona model on the TV series data set using different addressees and speakers. Adapted from <a class="ref-link" id="cLi_et+al_2016_b" href="#rLi_et+al_2016_b">Li et al (2016b</a>)
  • Table3: Ratings of three response generation systems on a 5K dialogue data set
  • Table4: Image commenting results of XiaoIce and four state-of-the-art image captioning systems, in percent. Adapted from <a class="ref-link" id="cHuang_et+al_2019_a" href="#rHuang_et+al_2019_a">Huang et al (2019</a>)
  • Table5: The record of the longest conversations of XiaoIce. We have verified carefully with these users that these long conversations are generated by XiaoIce and human users, not another bot
Download tables as Excel
Related work
  • XiaoIce is designed as a modular system based on a hybrid AI engine that combines rulebased and data-driven approaches, as presented in Figure 4 and Section 4. By contrast, in the research community, there is a growing interest in developing fully data-driven, end-to-end (E2E) systems for social chatbot (chitchat) scenarios, as reviewed in Chapter 5 of Gao, Galley, and Li (2019).

    The difference is mainly due to different design goals of social chatbots. Traditionally, social chatbots are designed for chitchat scenarios where the bots are expected to mimic human user conversations but not to interact with the user’s environment. For such scenarios, E2E approaches often lead to a very simple system architecture, such as RNNbased systems (Shang, Lu, and Li 2015; Vinyals et al 2015; Li et al 2016b), where the neural network–based response generation models can be easily trained on large-scale free-form, open-domain data sets (e.g., collected from social networks) to allow the bots to chat with users on any topics.

    XiaoIce, on the other hand, is designed as an AI companion that integrates both EQ and IQ skills that are needed to help users complete specific tasks. Thus, XiaoIce has to interact with the user’s environment and access real-world knowledge (e.g., via API calls). Therefore, XiaoIce uses a modular architecture similar to task-oriented dialogue systems, with different modules dealing with different tasks. Depending on the availability of training data and knowledge bases for each individual task, either a rule-based method or a data-driven method, or a hybrid of both, is adopted for the task. For example, when asked “what is the weather tomorrow?,” E2E systems are likely to give a plausible but random response, such as “sunny” and “rainy,” due to the lack of grounding in realworld knowledge.12 XiaoIce, however, generates a factual response based on the user’s geographical location and the corresponding database, as shown in Figure 19(a).
Funding
  • We find that incorporating the neuralbased generator into the baseline improves the coverage by 20%, and incorporating the retrieval-based generator using unpaired database into the baseline improves the coverage by 10%
Study subjects and analysis
active users: 660000000
We show how XiaoIce dynamically recognizeshuman 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 largescale 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

active users: 660000000
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

conversation pairs: 30000000000
First is the human conversational data from the Internet—social networks, public forums, bulletin boards, news comments, and so on. After the launch of XiaoIce in May 2014, we also started collecting human– machine conversations generated by XiaoIce and her users, which amounted to more than 30 billion conversation pairs as of May 2018. Nowadays, 70% of XiaoIce’s responses are retrieved from her own past conversations

pilot studies: 2
Evaluation. We present two pilot studies that validate the effectiveness of the personabased neural response generator and the hybrid approach that combines the generationbased and retrieval-based methods, respectively, and then the A/B test of General Chat. In the first pilot study reported in Li et al (2016b), we compare the persona model against two baseline models, using a TV series data set for model training and evaluation

users: 4000000
These skills allow XiaoIce to collaborate with human users in their creative activities, including text-based Poetry Generation,10 voice-based Song and Audio Book Generation, XiaoIce FM for Somebody, XiaoIce Kids Story Factory, and so on. The XiaoIce Poetry Generation skill has helped over four million users to generate poems. On 15 May 2018, XiaoIce published the first AI-created Chinese poem album in history.11

conversations with humans: 10000000000
In two months, XiaoIce successfully became a cross-platform social chatbot. Through August 2015, XiaoIce has had more than 10 billion conversations with humans. By that point, users have proactively posted more than 6 million conversation sessions to the public

active users: 660000000
XiaoIce has made these characters “alive” by bringing various capabilities including chatting, providing services, sharing knowledge, and creating contents. As of July 2018, XiaoIce has been deployed on more than 40 platforms, and has attracted 660 million active users. XiaoIce-generated TV and radio programs have covered

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