When and Who? Conversation Transition Based on Bot-Agent Symbiosis Learning Network (2020)

Abstract: In online customer service applications, multiple chatbots that are specialized in various 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 conversation 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.

## introduction recent years have witnessed a plethora of chatbot-based online customer services @xcite @xcite . 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 bot-agent symbiosis online customer service, or a symbiosis for short. in the symbiosis, the users only †corresponding author. *both authors contributed equally. this work is licensed under a creative commons attribution 4.0 international licence. licence details: http://creativecommons.org/licenses/by/4.0/ . need to interact with the system as a whole, instead of figuring out their own ways by speaking to various sources. see figure 1 for the comparison between a traditional bot-agent hybrid system and a symbiosis. in our own practice of building towards the symbiosis, two critical questions that are not well answered by the current nlp techniques are identified, namely, when should the transition occur and who the conversation session should be transferred to. the who problem is considered to be more difficult than the when problem as the former requires a broader understanding about agent profiles while the later only involves only the current dialogue. in this work, we propose a framework to utilize historical user feedback in order to handle these two problems in one go. the essential idea is to develop a result-driven symbiosis that attempts to maximize user satisfaction so that when the user is predicted to be upset by the current customer support, the transition will be triggered and another (human or bot) agent who is expected to be good at handling the current dialogue will be automatically assigned to take over the current session. ## related works different from the call center scheduling which aims to make the calling operation efficient and productive on the basis of knowledge of agents and their schedules @xcite @xcite @xcite , the bot-agent symbiosis is a relatively new concept and the conversation transition is a new problem owing to the emerging of chatbot-based customer services. as briefly touched in the last section, the conversation transition contains the when question, which can be treated as predicting the satisfaction level in real-time, and the who question, which can be treated as understanding what kind of skills are most needed from the agent based on the current session. predicting the satisfaction level of a dialogue can be considered as a scalar regression problem while understanding the required skills is a typical text classification problem. however, due to the fact that current nlp datasets are seldom satisfaction level annotated, the regression problem is little discussed. traditional methods to assess the satisfaction level is to gather feedback from the customer via survey after the dialogue ends. and some works tried to get the real time feedback through sentiment analysis of the utterances @xcite . however, our work shows that sentiment information is not enough to convey the satisfaction level. most existing text classification research focus more on monologue text @xcite @xcite rather than multi-turn conversations between multiple speakers. compared to text classification, multi-turn conversation classification is more complicated due to its nature of interaction. related works include dialogue act detection in meetings, customer service, online forum, etc. previous works from @xcite commonly aim to understand how utterances from multiple speakers relate to the roles of the speakers. to the best of our knowledge, our work is the first to formalize the conversation transition problem and the first to attempt the problem with a satisfaction driven approach. ## bot-agent symbiosis conversation transition the user satisfaction feedback is crucial to most symbiosis design, as the ultimate goal of the symbiosis is well paralleled to maximizing the user satisfaction. a lot of existing systems do employ similar feedback mechanics yet how exactly can the feedback be harnessed to improve the customer service is not very well understood. suppose the users seek various supports requiring different skill sets from the customer service and there are ways that the user can submit their feedback in the form of a score or indicator, we propose the following framework for building a bot-agent symbiosis. here we formalize the type classification and satisfaction level prediction problem, starting from the notations. a conversation or a dialogue is composed of multiple utterances from two speakers, namely, the user and the customer support. the user and the custom support are denoted by a and b respectively. a conversation is denoted by the following set now the when question is determined by a function h( p, l). and the who question can be answered by another function e( ĉ, z, l). we call these two functions when and who transition functions which can then be decided dependently and we will show our specific implementation in the following sections. ## deep neural networks methods figure 2 is the overall architecture of our proposed model. the entire network stacks up three different layers: firstly a convolutional neural network, secondly a bidirectional recurrent neural network and finally an attention layer. each layer has its unique role in transforming the plain text to either the types label or regressed satisfaction level. we detail how each layer works in the following subsections.