  Dialogue state tracking (DST) aims to convert the dialogue history into
dialogue states which consist of slot-value pairs. As condensed structural
information memorizing all history information, the dialogue state in the last
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 last 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 context matching framework is designed to narrow the
representation distance between a state and its corresponding 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 and improving anti-noise
ability.
