When and Who? Conversation Transition Based on Bot-Agent Symbiosis Learning Network

Proceedings of the 28th International Conference on Computational Linguistics, pages 4056–4066 Barcelona, Spain (Online), December 8-13, 2020 4056 When and Who? Conversation Transition Based on Bot-Agent Symbiosis Learning Network Yipeng Yu1∗†,Ran Guan2∗,Jie Ma3,Zhuoxuan Jiang4,Jingchang Huang3 Tencent, Shanghai, China1 University of Cambridge, Cambridge, UK2 IBM Research China, Beijing, China3 JD AI Research, Shanghai, China4 ianyu@tencent.com, ran.guan@cl.cam.ac.uk, jiema.uk@gmail.com jiangzhuoxuan@jd.com, hjingchang@foxmail.com Abstract In online customer service applications, multiple chatbots that are specialized in var- ious topics are typically developed separately and are then merged with other human agents to a single platform, presenting to the users with a unified interface. Ideally the conversation can be transparently transferred between different sources of customer support so that domain-specific questions can be answered timely and this is what we coined as a Bot-Agent symbiosis. Conversation transition is a major challenge in such online customer service and our work formalises the challenge as two core problems, namely, when to transfer and which bot or agent to transfer to and introduces a deep neural networks based approach that addresses these problems. Inspired by the net promoter score (NPS), our research reveals how the problems can be effectively solved by providing user feedback and developing deep neural networks that predict the con- versation category distribution and the NPS of the dialogues. Experiments on realistic data generated from an online service support platform demonstrate that the proposed approach outperforms state-of-the-art methods and shows promising perspective for transparent conversation transition. 1 Introduction Recent years have witnessed a plethora of chatbot-based online customer services (Oraby et al., 2017; Xu et al., 2017; Jiang et al., 2019). Although current chatbots are far from perfect, they are playing increasingly larger roles thanks to more advanced natural language processing (NLP) capabilities and can now respond properly to certain domain-specific problems and do some basic housekeeping tasks before a human agent gets involved. It is also common that the customer support is sourced to multiple agents and bots where the skill sets may vary greatly. Conventionally, users are firstly greeted by a porter bot, and then they could be further transferred to a human agent if they are not happy with the bot’s service. Human agent is usually assigned randomly based on availability rather than skill sets and the agent may refer the conversation to another more specialized agent if necessary. Therefore the users may have to navigate themselves among different sources of customer support explicitly or are guided by a customer support, either a bot or a human agent, whose job is quite similar to a switchboard operator before the users can receive corresponding customer service. However, the aforementioned conversation transition could be largely automated so that the users may not be aware of whether the underneath customer support has been switched, i.e., the conversation transition is transparent to the users. The conversation can also be directed to the customer support with the correct domain knowledge given the questions raised by the users can be well understood automatically. This hybrid system is referred as a …

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