These characters include more than 60,000 official accounts—for example, Lawson and Tokopedia’s customer service bots, Pokemon, Tecent and Netease’s chatbots, and even real human celebrities such as the singers of Guoyun Entertainment. XiaoIce has made these characters “alive” by bringing various capabilities including chatting, providing services, sharing knowledge, and creating contents. Figure 14 shows a few example comments generated by the competing systems in Table 4. Implementation of the conversation engine relies heavily on A/B testing to evaluate if a new module or a new dialogue skill is going to improve an existing component. This is possible because XiaoIce has attracted a large number of active users since her launch in 2014.

For example, the model produces “Of course, I love you, Emily,” in response to the input from Emily, and generates “Of course I love you. Social chatbots require a sufficiently high intelligence quotient to acquire a range of skills to keep up with the users and help them complete specific tasks. More importantly, social chatbots also require a sufficient emotional quotient to meet users’ emotional needs, such as emotional affection and social belonging, which are among the fundamental needs for human beings . Integration of both IQ and EQ is core to XiaoIce’s system design. Section2 casts a social chat as a hierarchical decision-making process using the mathematical framework of options over MDPs. Although the formulation provides a useful design principle, it remains to be proved the effectiveness of having a unified modeling framework for system development.

Growing A Business

In summary, these features can be grouped into four categories. 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 Conversational AI Key Differentiator 9 top satellite TV stations, and have attracted audiences of over 800 million weekly active viewers. Figure 15 shows that a user uses XiaoIce to make an FM program for her mother for the coming Chinese Spring Festival.
Another user, meanwhile, reported the bot kept sending them photos of scantily clad women. Li Di, CEO of Xiaoice, embraces the idea that his company provides comfort to marginalized social groups. “If our social environment were perfect, then Xiaoice wouldn’t exist,” he tells Sixth Tone. Yet despite his efforts to make a better life for himself, the young man feels trapped. He left vocational school and moved to a nearby town a few years ago, where he worked as a photo editor touching up family portraits. But things didn’t work out as well as he’d hoped, and he eventually moved back to his home village in Wan’an County.

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However, as argued by Gao, Galley, and Li , machine-learned metrics lead to potential problems such as overfitting and ”gaming of the metric” . For example, Sai et al. showed that ADEM can be easily fooled with a variation as simple as reversing the word order in the text. Their experiments on several such adversarial scenarios draw out counter intuitive scores on the dialogue responses. There has been significant debate as to whether these automatic metrics are appropriate for evaluating conversational response generation systems. Liu et al. argued that they are not by showing that most of these metrics (e.g., BLEU) correlate poorly with human judgments. But, as pointed out in Gao, Galley, and Li , the correlation analysis by Liu et al. is performed at the sentence level whereas BLEU is designed from the outset to be used as a corpus-level metric. Galley et al. showed that the correlation of string-based metrics (e.g., BLEU and deltaBLEU) significantly increases with the units of measurement longer than a sentence. Nevertheless, in open-domain dialog systems, the same input may have many plausible responses that differ in topics or contents significantly. Therefore, low BLEU scores do not necessarily indicate low quality, as the number of reference responses is always limited in the test set. An example of generating response candidates using the unpaired database and the XiaoIce knowledge graph , for which we show a fragment of the XiaoIce KG that is related to the topic “Beijing” .

This lack of predictability is another key feature of a human-like conversation. If it’s something she doesn’t know much about, she will try to cover it up. If that doesn’t work, she might become embarrassed or even angry, just like a human would. After this second removal, Xiaoice’s fans worried the bot was going to disappear completely. Li refused to comment on the issue with Sixth Tone, but pointed out that the company has taken strong action to ensure Xiaoice avoids crossing the line in the future.

The response candidates generated by three generators are aggregated and ranked using a boosted tree ranker (Wu et al. 2010). Figure 8 illustrates the process of generating response candidates using the unpaired database. Broadly speaking, chatbots have used two approaches to achieve this goal. You can attempt to hand-write responses to virtually every given input, as Steve Worswick did with his Mitsuku bot (which remains the closest bot to winning a Turing-like test). The advantage is that your responses always make sense and sound like a similar character, and your bot can’t be corrupted like an earlier attempt from Microsoft was. It was designed to hook users through lifelike, empathetic conversations, satisfying emotional needs where real-life communication too often falls short. Chatbots have come a long way from the early versions back in the 1960s — yes, the 1960s — such as ELIZA, who imitated a psychotherapist through pattern matching and response selection technology. Today, for people who want their questions answered quickly, the lowly chatbot is overwhelmingly chosen over other online forms. Woebot, another chatbot aimed at mental health, is also gaining ground, but there are far fewer users of such platforms in the United States than in China.

  • Eliza—a computer program for the study of natural language communication between man and machine.
  • The new topic is chosen by a machine-learned boosted tree ranker based on the following features.
  • The evaluation methodology eliminates many possibilities of gaming the metric.
  • XiaoIce’s IQ is shown by a collection of specific skills and Core Chat.
  • In the A/B test we observe that Image Commenting doubles the expected CPS across all dialogues that contain images.

Take the XiaoIce persona designed for WeChat deployed in China as an example. Our finding is that the majority of the “desired” users are young, female users. Therefore, we design the XiaoIce persona as an 18-year-old girl who is always reliable, sympathetic, affectionate, and has a wonderful sense of humor. Despite being extremely knowledgeable due to her access to large amounts of data and knowledge, XiaoIce never comes across as egotistical and only demonstrates her wit and creativity when appropriate. As shown in Figure 1, XiaoIce responds sensibly to some sensitive questions (e.g., Session 20), and then skillfully shifts to new topics xiaoice chatbot online that are more comfortable for both parties. As we are making XiaoIce an open social chatbot development platform for third-parties, the XiaoIce persona will be configurable based on specific user scenarios and cultures. 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. The use of neural response generation models in Core Chat, starting from the 5th generation, significantly improves the coverage and diversity of XiaoIce’s responses.

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