Scalable- DSC : A Structural Template Prompt Approach to Scalable Dialogue State Correction (2023)

Abstract: Dialogue state error correction has recently been proposed to correct wrong slot values in predicted dialogue states, thereby mitigating the error propagation problem for dialogue state tracking (DST). These approaches, though effective, are heavily intertwined with specific DST models, limiting their applicability to other DST models. To solve this problem, we propose Scalable Dialogue State Correction (Scalable-DSC), which can correct wrong slot values in the dialogue state predicted by any DST model. Specifically, we propose a Structural Template Prompt (STP) that converts predicted dialogue state from any DST models into a standardized natural language sequence as a part of the historical context, associates them with dialogue history information, and generates a corrected dialogue state sequence based on predefined template options. We further enhance Scalable-DSC by introducing two training strategies. The first employs a predictive state simulator to simulate the predicted dialogue states as the training data to enhance the generalization ability of the model. The second involves using the dialogue state predicted by DST as the training data, aiming at mitigating the inconsistent error type distribution between the training and inference. Experiments confirm that our model achieves state-of-the-art results on MultiWOZ 2.0-2.4.

## introduction task-oriented dialogue systems are becoming increasingly important in facilitating the daily life of human beings. dialogue state tracking module plays an important role in dialogue systems @xcite , which aims to accurately track the user's goals based on the dialogue history and † equal contribution. ‡ corresponding author. △ our code is available at https://github.com/ xjuspeech/scalable-dsc ## methodology figure 2 illustrates the overall framework of correcting erroneous slot values in dialogue states using scalable-dsc. in the preprocessing section, strategy 1 employs a predictive state simulator to simulate predicted dialogue states in the training set. strategy 2 utilizes a dst model to generate predicted dialogue states in the training set. in the training section, we utilize the predicted dialogue states b pred_seq t generated from the preprocessing section as a part of the input schema for the stp method in scalable-dsc, resulting in the generation of the corrected target sequence b error_seq t ⊕ b correct_seq t . in the inference section, scalable-dsc corrects the prediction results of the dst model on the test set. ## related work early dst methods @xcite @xcite ) relied on pre-defined rules to recognize dialogue states. although good prior knowledge can solve cold-start performance issues, it requires a large amount of manual rule-making, is inflexible, lacks scalability, and cannot simultaneously track multiple types of state information. subsequently, some classification dst models based on a predefined ontology were proposed @xcite @xcite , which significantly improved the model performance. however, it is difficult to design a complete and robust pre-defined ontology file @xcite . therefore, most researchers focus on developing open-vocabulary dst models @xcite @xcite @xcite , which extract dialogue states directly from the complete dialogue history, ignoring the issue of incomplete context information caused by truncation of dialogue history. to address this challenge, @xcite @xcite attempts to track users' goals from the context of the dialogue and the previous dialogue state. however, error propagation has become the reason for limiting model performance. to address this problem, the idea of correcting dialogue state is proposed @xcite , but the methods heavily rely on specific dst models, i.e., lacking scalability. prompt learning is a widely used technique in natural language processing (nlp), aiming at reducing the gap between the target before training and downstream tasks @xcite . some recent prompt-based dst work has been proposed @xcite @xcite . they use different text prompts to provide task information to the model. for example, @xcite uses slot names for prompt information, @xcite uses slot description, @xcite prompt information is possible values. some other work @xcite has proposed the use of automatic prompts that do not require manually predefined prompts. ## conclusion and future work we have proposed scalable-dsc, a new dialogue state correction model aiming at decoupling the error correction functionality from a specific dst model, i.e., scalability, by introducing the stp strategy. the key idea of stp is to convert the dialogue state into a natural language sequence and generates a corrected dialogue state natural language sequence based on the guidance of template options, combined with real dialogue history information. extensive experiments have been carried out to analyze the scalability and correction performance of the model. results have confirmed the applicability of the model to other dst models and demonstrated a new state-of-the-art performance on multiwoz2.0-2.4. as discussed, limitation still exists. how to better correct all types of errors in dialogue states and avoid the generation of new errors remains interesting research topic for the future work.