Correctable- DST : Mitigating Historical Context Mismatch between Training and Inference for Improved Dialogue State Tracking (2022)

Abstract: Recently proposed dialogue state tracking (DST) approaches predict the dialogue state of a target turn sequentially based on the previous dialogue state. During the training time, the ground-truth previous dialogue state is utilized as the historical context. However, only the previously predicted dialogue state can be used in inference. This discrepancy might lead to error propagation, i.e., mistakes made by the model in the current turn are likely to be carried over to the following turns.To solve this problem, we propose Correctable Dialogue State Tracking (Correctable-DST). Specifically, it consists of three stages: (1) a Predictive State Simulator is exploited to generate a previously “predicted” dialogue state based on the ground-truth previous dialogue state during training; (2) a Slot Detector is proposed to determine the slots with an incorrect value in the previously “predicted” state and the slots whose values are to be updated in the current turn; (3) a State Generator takes the name of the above-selected slots as a prompt to generate the current state.Empirical results show that our approach achieves 67.51%, 68.24%, 70.30%, 71.38%, and 81.27% joint goal accuracy on MultiWOZ 2.0-2.4 datasets, respectively, and achieves a new state-of-the-art performance with significant improvements.

## introduction dialogue state tracking is the core module of a taskoriented dialogue system. it extracts the user's goal in each turn and represents the dialogue state as a set of (slot, value) pairs. its performance will affect the decision prediction of the dialogue system. traditional neural network-based dst casts the prediction of slot values into a classification task (mrkšić et al., 2017; liu and lane, 2017; zhong et al., 2018; ren et al., 2018; nouri and † corresponding author. hosseini-asl, 2018), requiring a predefined ontology which includes all candidate slot-value pairs. however, some undefined slot values @xcite will appear in real scenarios. therefore, current dst research focuses mainly on open vocabulary dst @xcite @xcite @xcite @xcite , where the value of each slot is generated or extracted based on the dialogue history to resolve scalability and generalization issues of the predefined ontology-based approach, but these approaches often lack efficiency because they predict the dialogue state from scratch at every dialogue turn. some approaches utilize the dialogue state of the previous turn as a compact representation of the dialogue history and, based on that, generate the slot values to improve efficiency. @xcite ; @xcite decompose the dst into two tasks: state operation prediction and value generation. but the performance of the state operation prediction will affect the performance of dst @xcite . therefore, @xcite ; @xcite ; @xcite ; @xcite proposed to jointly model state operation prediction and value generation in an implicit way, and the dialogue state tracking is re-transformed into a single causal language model. although these methods also work reasonably well, they suffer from error propagation @xcite . figure 1 illustrates the same errors that will appear in later dialogue turns, and dialogue state errors can be divided into three types @xcite : unfortunately all we have is the aylesbray lodge guest house. okay , do you have any hotel with a 3 star rating instead ? can i help more today ? ground trouth dialogue states (training) predicted dialogue states (inference) hotel @xmath0,@xmath1, @xmath2,@xmath3, @xmath4, @xmath5,@xmath6, @xmath7, @xmath8,@xmath9, @xmath10, @xmath11, ... ,book @xmath12,@xmath13,@xmath14, @xmath15,@xmath16, @xmath17,@xmath18, @xmath19, @xmath20, ... ,book @xmath21. in the 3rd turn, the value of hotel-stars is predicted to "4". there are two factors contributing to the error propagation problem: (1) historical context mismatch between training and inference. using the dialogue state of the previous turn as a compressed representation of the dialogue history, i.e., historical context, that is fed to the model is reasonable during training because the dialogue state of the previous turn is always correct. however, in the inference time, the previously predicted dialogue state may contain incorrect slot values; (2) these models cannot determine and correct errors in the previously predicted dialogue state. because these models do not use the complete dialogue history as the input, they cannot compare the consistency of information between the previous dialogue history and the previously predicted dialogue state. based on the above, we propose a correctable dialogue state tracking (correctable-dst) model, as illustrated in figure 2 . we use the predictive state simulator to generate a pseudo "predicted" dialogue state, i.e., one that randomly inserts and deletes slot values based on the ground-truth dialogue state of the previous turn to alleviate the historical context mismatch. then, the slot detector judges the slots with redundant value and missing value in the previously "predicted" dialogue state. finally, when generating the current turn state, the name of the above slots is used as the prompt information fed to the state generator, thus enhancing the ability of the model to correct the overprediction and partial prediction. in addition, the slot detector also predicts the slots whose value needs to be updated compared with the previous turn. then it returns the slot's name to the state generator to improve the model's update ability, thus reducing the error propagation caused by the erroneous prediction. dialogue history is also used as an input to the model, which is a prerequisite for enabling the model to determine and correct errors in the previous dialogue state. we evaluate the effectiveness of our model on multiwoz 2.0-2.4 datasets. experimental results show that our model reaches 67.51%, 68.24%, 70.30%, 71.38% and 81.27% joint goal accuracy, outperforming previous strong baselines by +10.58%, +7.51%, +9.81%, +5.51% and +6.64%, respectively. furthermore, a series of subsequent ablation studies were conducted to demonstrate the effectiveness of the proposed method. our contributions are as follows: ## proposed approach figure 2 illustrates the architecture of correctable-dst, which includes predictive state simulator, slot detector, and state generator. in this section, we elaborate on each module of this approach. with t turns hotel @xmath22, @xmath23, @xmath24, @xmath25: the overview of the proposed correctable-dst. the model first generates previously "predicted" dialogue state bt-1 that may contain errors by predictive state simulator in training time, then slot detector obtains slot information s over t-1 , s lack t-1 , and s update t-1 . finally, the name of the above slot information is used as the prompt information fed to the state generator. ## related work traditional dst models formulate dst as a value classification task for each slot, assuming all values are available @xcite @xcite . in practice, this is a limiting assumption because there are a large number of possible values in real life, and it is impractical to enumerate all possible values. therefore, the current research work mainly focuses on the open vocabulary dst @xcite @xcite @xcite @xcite , where the value of each slot is generated or extracted based on the dialogue history. @xcite handle this problem by decomposing dst into two sub-tasks: state operation prediction and value generation. at each turn, whether the value in the previous dialogue state is modified or not or how to modify the value is determined by the discrete operations predicted by the state operation. many recent works @xcite follow this approach. besides, some works @xcite @xcite put forward jointly model state operation prediction and value generation in an implicit way, which means the current turn of dialogue and the previous dialogue state are used as input sequences, and the dialogue state tracking is re-transformed into a single causal language model. in addition, some task-oriented pre-training models have been proposed, hosseini-asl et al. (2020) used a single, causal language model trained on all sub-tasks recast as a single sequence prediction problem. @xcite introduced a new dialogue multi-task pre-training strategy that allows the model to learn the primary tod task completion skills from heterogeneous dialogue corpora. @xcite formalizes dst as a sequence-to-sequence problem. @xcite is acquired by fine-tuning the large pretrained unidirectional language model gpt-2 on the sequence of the entire dialogue session. due to some methods @xcite @xcite @xcite suffer from error propagation @xcite . @xcite propose an amendable generation method which improves the output dialogue state through a two-pass decoding process, this is similar to automatic postediting methods in the field of machine translation @xcite , there has first been a decoder producing the primitive output, to which it adds a second decoder, creating an improved version of that output. but the dst model improved by "two-pass decoding" may still produce wrong output, and the error will be propagated when predicting the subsequent states. ## conclusion in this paper, we propose a new correctable dialogue state tracking approach. this approach generate the previous "predicted" dialogue state during training through the predictive state simulator. then, the slot detector outputs the slot information. finally, the state decoder will correct the errors in the previously "predicted" dialogue state according to the slot information and reduce the current erroneous prediction to alleviate the error propagation problem. experimental results show that our model achieves the state-of-the-art performance of 67.51%, 68.24%, 70.30%, 71.38%, and 81.27% achieving significant improvements (10.58%, 7.51%, 9.81%, 5.51%, and 6.64%) joint goal accuracy over the previous best results on the multiwoz 2.0-2.4.