Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks (2021)

Abstract: Dialogue state tracking (DST), which estimates user goals given a dialogue context, is an essential component of task-oriented dialogue systems. Conventional DST models are usually trained offline, which requires a fixed dataset prepared in advance. This paradigm is often impractical in real-world applications since online dialogue systems usually involve continually emerging new data and domains. Therefore, this paper explores Domain-Lifelong Learning for Dialogue State Tracking (DLL-DST), which aims to continually train a DST model on new data to learn incessantly emerging new domains while avoiding catastrophically forgetting old learned domains. To this end, we propose a novel domain-lifelong learning method, called Knowledge Preservation Networks (KPN), which consists of multi-prototype enhanced retrospection and multi-strategy knowledge distillation, to solve the problems of expression diversity and combinatorial explosion in the DLL-DST task. Experimental results show that KPN effectively alleviates catastrophic forgetting and outperforms previous state-of-the-art lifelong learning methods by 4.25% and 8.27% of whole joint goal accuracy on the MultiWOZ benchmark and the SGD benchmark, respectively.

## introduction task-oriented dialogue systems aim at helping users to accomplish various tasks, such as reserving restaurants, booking flights, and checking weather @xcite @xcite . dialogue state tracking (dst) is an essential component of task-oriented dialogue systems, which estimates user goals for downstream modules @xcite @xcite @xcite . given a user utterance and its dialogue history, a dst model should be able to output an accurate dialogue state. in general, the dialogue state is represented as a set of slot-value pairs, such as ((restaurant-area, north), (restaurantprice, expensive)). previous dst models are usually trained offline, which requires a fixed dataset prepared in advance. these offline solutions are often impractical in realworld applications, as online dialogue systems usually involve continually emerging new data and domains, especially when new services are introduced. in addition, it is infeasible to retrain dst models from scratch every time new domain data arrives due to computational costs, storage budgets, and data privacy @xcite . to tackle this realistic issue, we explore domain-lifelong learning for dialogue state tracking (dll-dst). as shown in figure 1 , the dll-dst task aims to continually train a dst model on new data to learn incessantly emerging new domains. at each step, new data generally contains one or multiple new domains, and the updated model should be able to make accurate predictions for all the domains observed so far. a plain approach to domain-lifelong learning is to simply fine-tune a pre-trained model on new data. however, this approach suffers from the problem of catastrophic forgetting @xcite . to be more specific, fine-user 1: i want an upscale restaurant in the northern part. ## method in this work, we propose knowledge preservation networks (kpn) to handle the dll-dst task. kpn consists of two core components, i.e., multi-prototype enhanced retrospection and multistrategy knowledge distillation, for dealing with the two main challenges, i.e., expression diversity and combinatorial explosion. the framework of kpn is shown in figure 3 . ## conclusion in this paper, we introduce domain-lifelong learning into dialogue state tracking and propose knowledge preservation networks to overcome catastrophic forgetting. to handle expression diversity, we propose multi-prototype enhanced retrospection to reserve the most representative samples. moreover, to alleviate the adverse effects of combinatorial explosion, we propose multi-strategy knowledge distillation to learn the previous knowledge of the original model. experimental results on the multiwoz and sgd benchmarks demonstrate the effectiveness of our model.