Continual Dialogue State Tracking via Example-Guided Question Answering (2023)

Abstract: Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on previously learnt services. Motivated by the insight that dialogue state tracking (DST), a crucial component of dialogue systems that estimates the user’s goal as a conversation proceeds, is a simple natural language understanding task, we propose reformulating it as a bundle of granular example-guided question answering tasks to minimize the task shift between services and thus benefit continual learning. Our approach alleviates service-specific memorization and teaches a model to contextualize the given question and example to extract the necessary information from the conversation. We find that a model with just 60M parameters can achieve a significant boost by learning to learn from in-context examples retrieved by a retriever trained to identify turns with similar dialogue state changes. Combining our method with dialogue-level memory replay, our approach attains state of the art performance on DST continual learning metrics without relying on any complex regularization or parameter expansion methods.

## introduction as conversational digital assistants are becoming increasingly popular and versatile, it is important to continuously update them to accommodate more services. 1 one of their key components is a dialogue state tracking (dst) model that estimates the user's goal, i.e. the dialogue state @xcite . the dialogue state is used for queries sent to application programming interfaces to retrieve figure 1 : left: when continually learning with the original dst format, dst models need to memorize new slot keys when learning each subsequent service. right: instead, reformulating dst into a bundle of granular question answering tasks with help from similar examples (symbolized by the light bulbs) makes training data uniform across all services. learning new services effectively becomes additional training for the general task of example-guided question answering and is more conducive to continual learning. unfortunately, naively updating a model for a new service by training with new data causes catastrophic forgetting @xcite : upon learning from new data, the model's performance for previous services regresses. to mitigate this issue while also avoiding the impracticality of training a model from scratch with data from all services each time new data becomes available, three main approaches have been established as generally effective approaches to continual learning (cl): memory replay, regularization, and parameter expansion. variations and combinations of the three have been applied for dst in previous work @xcite @xcite . however, most previous work has focused on improving cl performance with service-specific inputs or outputs, a paradigm that limits knowledge transfer between services (left side of figure 1 ). this approach introduces a large distribution shift from one service to another since the model needs to memorize service-specific slots that it needs to predict as part of the output. however, dst can become a significantly more consistent task across services by simply reformulating it as a collection of example-guided question answering tasks. our approach, dialogue state tracking as example-guided question answering (dst-egqa foot_1 ) trains a model to learn to answer natural language questions that correspond to dialogue state slots (right side of figure 1 ) with the help of in-context examples instead of predicting service-specific structured outputs all at once without any explicit guidance (left side of figure 1 ). we hypothesize that dst-egqa benefits continual learning because it transforms the dst task to become more granular, easy, and consistent across services. we discover that this is indeed the case, as our approach leads to significant gains in cl performance without using any of the aforementioned cl approaches or data augmentation methods. specifically, we transform dst into the transferqa @xcite format and add examples from a retriever that is trained to identify turns that result in similar dialogue state updates @xcite . in addition, our approach does not require complex partitioning of the full training set into training samples and retrieval samples. we find that we can use each sample in the training set as both target samples and examples in the retrieval database without causing any label leakage. also, we experiment with a wide array of retrievers and find that models trained to perform dst-egqa can be effective even with lower quality retrievers by intentionally training it with subpar examples such that it can learn when to leverage good examples and ignore bad ones. lastly, we simply tweak the sampling approach for memory replay to sample at the dialogue-level instead of the turn-level and achieve significant gains to cl performance even with a single dialogue sample, resulting in state-of-theart performance on the schema guided dialogue (sgd) dataset @xcite . in summary, our main contributions are: ## dialogue state tracking as example-guided question answering (dst-egqa) the goal of continual learning for dst is to sequentially train on a stream of n services t 1 ...t n with the goal of minimal degradation, i.e. catastrophic forgetting, of peak performance that was achieved when the model was trained on for each service t i . in this section, we motivate and elaborate on the methodology of dst-egqa for attaining this goal. figure 2 presents an illustrated overview. ## conclusion in this paper, we propose dialogue state tracking as example-guided question answering as a method for enhancing continual learning performance that factors dialogue state tracking into granular question answering tasks and fine-tunes the model to leverage relevant in-context examples to answer these questions. our method is an effective alternative to existing continual learning approaches that does not rely on complex regularization, parameter expansion, or memory sampling techniques. analysis of our approach reveals that even models as small as 60m parameters can be trained to perform in-context learning for continual learning and that complementing such a model with a randomly sampled memory achieves stateof-the-art results compared to strong baselines.