Gated Mechanism Enhanced Multi-Task Learning for Dialog Routing (2022)

Abstract: Currently, human-bot symbiosis dialog systems, e.g. pre- and after-sales in E-commerce, are ubiquitous, and the dialog routing component is essential to improve the overall efficiency, reduce human resource cost and increase user experience. To satisfy this requirement, existing methods are mostly heuristic and cannot obtain high-quality performance. In this paper, we investigate the important problem by thoroughly mining both the data-to-task and task-to-task knowledge among various kinds of dialog data. To achieve the above target, we propose a comprehensive and general solution with multi-task learning framework, specifically including a novel dialog encoder and two tailored gated mechanism modules. The proposed Gated Mechanism enhanced Multi-task Model (G3M) can play the role of hierarchical information filtering and is non-invasive to the existing dialog systems. Experiments on two datasets collected from the real world demonstrate our method’s effectiveness and the results achieve the state-of-the-art performance by relatively increasing 8.7%/11.8% on RMSE metric and 2.2%/4.4% on F1 metric.

## introduction traditionally, a lot of human resource cost is spent on supporting the calling/online centers for customer care, such as pre-and after-sales for ecommerce and banking. with the rapid development of ai techniques for dialog systems, various bot agents have been deployed in those scenarios to reduce parts of human workload. both the bot and human agents constitute a new and practical symbiosis dialog system which can keep a balance between service quality and human resource cost @xcite . in such a human-bot symbiosis environment, it is commonly seen that users' needs continuously * this work was done when they were in ibm research china, and zhuoxuan jiang is the corresponding author. dialog router bot agent skilled at simple queries human agent a skilled at 'technical issue' category human agent b skilled at 'completion issue' category ① ② … user human-bot hybrid dialog system user utterance nps dialog category … … … utterance i 6.5 finding content utterance i+1 5.8 finding content utterance i+2 4.3 completion issue … … … sorry for not being very helpful. you can't find the course in your learning queue and the progress are all gone, correct? you did not answer my question, i can't find the course and i want to resume it response module change, or sometimes they are dissatisfactory with the current servicing agents. they would request the dialog system to replace the agents with more qualified ones, because usually each bot or human agent is trained for specific domains. for example, some agents are good at handling after-sales complaints, while some others would do well in providing technical guidance. to make the whole system operate efficiently, a dialog routing component is necessary to take charge of when to transfer the conversation and which domain of agents is suitable to provide service. to illustrate the process of dialog routing task more clearly, figure 1 shows an example. at the beginning, the user's query is operated by the bot agent through flow 1 ⃝, and the dialog router would continuously monitor the user's satisfactory degree by inferring net promoter score (nps) and current dialog category. when the inferred nps (4.8) is lower than a hand-crafted threshold (5) at the ith dialog turn, the router chooses to switch the dialog to one of human agents skilled at the inferred category via flow 2 ⃝. here human agent b is good at the category of 'completion issue' dialogs. from this picture, we could realize that how to implement the accurate matching between user's needs and skilled agents is greatly correlated with the whole system's efficiency and user experience. currently, most existing deployed systems are heuristic and support to trigger the dialog routing when users actively click a button or say/type something like 'transfer to human'. this method is less user-friendly and cannot fully leverage the ability of contemporary deep leaning techniques. recently, some learning based methods are proposed. @xcite well defines the dialog routing as text regression and classification problems, and proposes a learning network with cnn and rnn modules to encode dialog data @xcite . however, it only models the token-level utterance data and misses the information from other kinds of dialog data 1 ; meanwhile the model structure is shared by the two tasks (in single-task way) which cannot capture the underlying knowledge within data-totask (intrinsic) and task-to-task (extrinsic) relations. in this paper, we investigate the dialog routing task from the multi-task learning perspective to further capture the intrinsic and extrinsic information. intuitively, the decision making process of dialog router could be decomposed to two subtasks, nps prediction and dialog category classification. we adopt the same nps prediction task as @xcite since the supervised data can be obtained without any human-labeling effort. furthermore, we observe that the data-to-task (intrinsic) and taskto-task (extrinsic) information should be leveraged to make better routing performance. on the one hand, we can argue that the more accurate nps is predicted, the more precise the dialog category is classified, and vice verse. on the other hand, in addition to the token-level utterance data, other kinds of dialog data can also affect the final task performance. for example, if a user utterance is about the intent of 'ask_technical_problem', the intent information is an obvious indicator that the user should be served by the agent who is skilled at 'technical issue' category. note that the categories of agents and dialog intents of users are often different, and usually the former is much less than the later. to achieve the above motivation, in this paper, we propose a novel gated mechanism enhanced multi-task model (g3m). firstly, the model extends the bert encoder to encode various kinds of dialog data in a hierarchical way. moreover, two modules of gated mechanism are proposed to explicitly model the data-to-task and task-to-task information under multi-task learning framework. 1 other kinds of dialog data include speaker roles, utterance-level sequence, intent information, etc. another advantage of g3m is its good compatibility, such that it can be easily integrated into existing human-bot dialog systems in a plug-in manner. we conduct various experiments on two datasets collected from the real world. the results demonstrate our method can achieve the state-of-the-art performance on both tasks, and the ablation experiment proves our model is effective to simultaneously capture the intrinsic and extrinsic information. in summary, this paper's contributions include: ## related work compared with other dialog system tasks, there are not many related work on dialog routing study. recently, a learning network with attention based cnn and rnn is proposed @xcite . it mainly models the token-level dialog data and is a single-task model, i.e., the nps regression and dialog classification tasks share the same model structure except the last inference layer. this method cannot sufficiently leverage the underlying knowledge within subtasks and various kinds of dialog data. another similar work is a demo system for dialog transition @xcite which tries a vanilla multi-task learning method (i.e., a dialog encoder tailed with two prediction subtasks). however, it still hardly fully utilizes the knowledge among dialog data and subtasks. different from them, we further investigate the problem by simultaneously modeling data-to-task and task-to-task information, achieving a much advanced performance. data-to-task weighted feature 𝑔 (*) sion and dialog classification, which feature encoding various kinds of dialog data and have been separately studied by researchers from the nlp community. the representative research studies include cnn-based models @xcite , rnnbased models @xcite , regression-based models @xcite and deep bi-directional transformers model with pre-training @xcite . from these works, we learn that dialog data encoder is the common key module to make sure good performance on final tasks @xcite . those methods have not been demonstrated to be effective in the multi-task situation of dialog routing, without thoroughly mining the relations among data and subtasks. recently, multi-task learning on dialog data has been studied and proven successful, such as joint slot filling and intent prediction @xcite , dialog act sequence labeling @xcite , and dialog response generation @xcite . these studies suggest that dialog data contains rich information and multi-task learning could dig out the underlying knowledge among data and subtasks. moreover, among multi-task learning methods, multi-gated mechanism is widely utilized for information remembering and filtering @xcite @xcite , which is much suitable for tidying dialog data. inspired by the above ideas, we propose a novel model to combine them together in our solution for further improving the performance of dialog routing task. ## problem definition we define the investigated problem formally in this section. in our task, we use the available data in a conversation session as inputs, including an utterance list @xmath0, ..., u l }, a speaker role list @xmath1, ..., r l }, and an intent list @xmath2, ..., e l }, where u i is an utterance, r i indicates the role of the ith utterance (user or agent), e i is known intent of the ith utterance, and l is the number of utterance sequences in a conversation session. each utterance has a token-level sequence u @xmath3, ..., w im }, where w ij means the jth token in the ith utterance and m is the length of the token sequence. here we can leverage the intent information since we use an intent extraction tool foot_0 . our model is also scalable to capture more dialog data, such as entity and dialog act information if they are available. we adopt the intent information in our solution based on the intuitive consideration of data-to-task motivation. for the outputs of our task, at each dialog turn i, our model can make predictions of nps y ( the proposed model generally contains three parts: ## conclusion in current ubiquitous human-bot symbiosis dialog systems for customer care, the dialog routing component is necessary to improve the overall system efficiency, reduce human resource cost, and enhance user experience. in this paper, we argue that the data-to-task and task-to-task information among various kinds of dialog data and subtasks should be jointly leveraged to perform better dialog routing ability. we propose a gated mechanism enhanced multi-task model (g3m) to implement that motivation. specifically, we design a new dialog encoder to learn various kinds of dialog data by extending the bert encoder, and two gated mechanism modules are proposed to capture datato-task and task-to-task information. extensive experiments on two real-world datasets demonstrate the effectiveness of our proposed model, which can achieve the state-of-the-art performance.