M o NET : Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking (2023)

Abstract: Dialogue state tracking (DST) aims to convert the dialogue history into dialogue states which consist of slot-value pairs. As condensed structural information memorizes all history information, the dialogue state in the previous turn is typically adopted as the input for predicting the current state by DST models. However, these models tend to keep the predicted slot values unchanged, which is defined as state momentum in this paper. Specifically, the models struggle to update slot values that need to be changed and correct wrongly predicted slot values in the previous turn. To this end, we propose MoNET to tackle state momentum via noise-enhanced training. First, the previous state of each turn in the training data is noised via replacing some of its slot values. Then, the noised previous state is used as the input to learn to predict the current state, improving the model’s ability to update and correct slot values. Furthermore, a contrastive contextmatching framework is designed to narrow the representation distance between a state and itscorresponding noised variant, which reduces the impact of noised state and makes the model better understand the dialogue history. Experimental results on MultiWOZ datasets show that MoNET outperforms previous DST methods. Ablations and analysis verify the effectiveness of MoNET in alleviating state momentum issues and improving the anti-noise ability.

## introduction dialogue state tracking (dst) is a core component in modular task-oriented dialogue systems @xcite @xcite . it extracts users' intents from the dialogue history and converts them into i need a hotel reservation for 4 nights starting saturday. i am sorry, but there is nothing available at that time at either hotel. would you like to try fewer nights or perhaps a different arrival day? will you try sunday arrival? yes, i tried sunday but it is not available. can you shorten your stay at all? yes, can you try it for 3 nights? turn 1 turn 2 turn 3 gt: hotel-book day-sunday pred: hotel-book day-saturday gt: hotel-book day-saturday pred: hotel-book day-saturday gt: hotel-book day-sunday pred: hotel-book day-saturday r u r u r update correct figure 1: a dialogue example of three turns, containing the system utterance (u), the user response (r), the ground truth dialogue state (gt), and the prediction of each turn (pred). the state "hotel-book day-saturday" is predicted in the first turn (marked in blue). the dotted arrow represents the ideal predictions, i.e., update slot values that need to be changed (turn 2) and correct wrongly predicted slot values in the previous turn (turn 3). the solid arrow represents the predictions (marked in red) with state momentum issues. conventional dst models taking the previous state as the input usually show the characteristic that the previously predicted slot values tend to be kept unchanged when predicting the current state, defined as state momentum in this paper. the state momentum makes dst models struggle to modify the previous prediction, which affects the performance when the values of some slots need to be updated as the user's intent changes, and there exist wrongly predicted slot values that need to be corrected. figure 1 gives an example of a dialogue involving three turns with two types of state momentum issues. the state hotel-book day-saturday is predicted in turn 1 and keeps unchanged in the next two turns, while the user's request is updated into sunday in turn 2. consequently, the predicted state becomes wrong in the following two turns. the dotted arrow represents the ideal prediction cases: the value is updated with the ground truth changes and is corrected when becoming a wrong input. the solid arrow represents the state momentum issues, where the state is kept unchanged, leading to two consecutive wrong predictions. one possible reason for the state momentum issue is that in the training data, most slot values in the previous turn are the same as those in the current turn, which limits the ability of conventional dst models to modify slot values during inference. to address this limitation, an intuitive idea is to augment training instances with a higher ratio of slots whose previous values differ from those in the current turn. by incorporating such examples, the dst model can learn to deal with more cases where modifying previous predictions is required. besides, if the dst model can treat wrong and correct dialogue states similarly in representations, then the former will typically help make further predictions. in other words, by treating incorrect dialogue states as valuable information, the dst model can potentially identify and correct erroneous slot values. in this paper, we propose monet to tackle the state momentum issue via a noise-enhanced training strategy. the core idea is to manually add noise into the previous state to simulate scenarios with wrong state input. first, the previous state of each turn in the training data is noised via replacing some of its slot values. specifically, for each active slot (with a non-none value), we replace its value with a certain probability. then, the noised previous state, concatenated with the dialogue history, is used as the input to learn to predict the current state, improving the model's ability to update and correct slot values. furthermore, a contrastive context matching framework is designed to narrow the representation distance between a state and its corresponding noised variant, which reduces the impact of the noised state and makes the dst model better understand the dialogue history. such approaches make the model less sensitive to the noise, and enhance its ability to modify the slot values of previous states in current predictions. experiments on the multidomain dialogue datasets multiwoz 2.0, 2.1, and 2.4 show that our monet outperforms previous dst models. ablation studies and analysis further verify the effectiveness of the proposed noised dst training and the contrastive context matching framework in alleviating state momentum and improving the model's anti-noise ability. the contributions are summarized as follows: (1) we define the state momentum issue in dst, where models tend to keep the predicted slot values unchanged, namely, struggling to update and correct them from the previous turn. (2) we propose monet to tackle the state momentum issue via noised dst training and the contrastive context matching framework. (3) we conduct comprehensive experiments on three datasets, multiwoz 2.0, 2.1, and 2.4. the results demonstrate that monet outperforms previous dst methods, showcasing its effectiveness in alleviating the state momentum issue.