MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking

by   Haoning Zhang, et al.

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.


page 1

page 2

page 3

page 4


On Tracking Dialogue State by Inheriting Slot Values in Mentioned Slot Pools

Dialogue state tracking (DST) is a component of the task-oriented dialog...

Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking

The goal of dialogue state tracking (DST) is to predict the current dial...

Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking

In dialogue state tracking, dialogue history is a crucial material, and ...

Improving Long Distance Slot Carryover in Spoken Dialogue Systems

Tracking the state of the conversation is a central component in task-or...

Dialogue State Distillation Network with Inter-Slot Contrastive Learning for Dialogue State Tracking

In task-oriented dialogue systems, Dialogue State Tracking (DST) aims to...

Diable: Efficient Dialogue State Tracking as Operations on Tables

Sequence-to-sequence state-of-the-art systems for dialogue state trackin...

A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking

Recent studies in dialogue state tracking (DST) leverage historical info...

Please sign up or login with your details

Forgot password? Click here to reset