Log In Sign Up

Coreference Augmentation for Multi-Domain Task-Oriented Dialogue State Tracking

by   Ting Han, et al.

Dialogue State Tracking (DST), which is the process of inferring user goals by estimating belief states given the dialogue history, plays a critical role in task-oriented dialogue systems. A coreference phenomenon observed in multi-turn conversations is not addressed by existing DST models, leading to sub-optimal performances. In this paper, we propose Coreference Dialogue State Tracker (CDST) that explicitly models the coreference feature. In particular, at each turn, the proposed model jointly predicts the coreferred domain-slot pair and extracts the coreference values from the dialogue context. Experimental results on MultiWOZ 2.1 dataset show that the proposed model achieves the state-of-the-art joint goal accuracy of 56.47


Point or Generate Dialogue State Tracker

Dialogue state tracking is a key part of a task-oriented dialogue system...

Dialogue State Tracking with Multi-Level Fusion of Predicted Dialogue States and Conversations

Most recently proposed approaches in dialogue state tracking (DST) lever...

Dialogue State Tracking with Pretrained Encoder for Multi-domain Trask-oriented Dialogue Systems

In task-oriented dialogue systems, Dialogue State Tracking (DST) is a co...

Amendable Generation for Dialogue State Tracking

In task-oriented dialogue systems, recent dialogue state tracking method...

Towards Task-Oriented Dialogue in Mixed Domains

This work investigates the task-oriented dialogue problem in mixed-domai...

Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking

Dialogue state tracking (DST) aims to extract essential information from...

Learn to Focus: Hierarchical Dynamic Copy Network for Dialogue State Tracking

Recently, researchers have explored using the encoder-decoder framework ...