Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking

TL;DR

This paper introduces a novel fusion network that effectively encodes dialogue context and schema relations, achieving state-of-the-art results in multi-domain dialogue state tracking on benchmark datasets.

Contribution

The paper proposes a new context and schema fusion network utilizing internal and external attention mechanisms for improved multi-domain dialogue state tracking.

Findings

Achieves new state-of-the-art performance on MultiWOZ 2.0 and 2.1 datasets.

Effectively models relations among domain-slots using schema graphs.

Enhances dialogue state tracking accuracy with efficient context encoding.

Abstract

Dialogue state tracking (DST) aims at estimating the current dialogue state given all the preceding conversation. For multi-domain DST, the data sparsity problem is a major obstacle due to increased numbers of state candidates and dialogue lengths. To encode the dialogue context efficiently, we utilize the previous dialogue state (predicted) and the current dialogue utterance as the input for DST. To consider relations among different domain-slots, the schema graph involving prior knowledge is exploited. In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms. Experiment results show that our approach can obtain new state-of-the-art performance of the open-vocabulary DST on both MultiWOZ 2.0 and MultiWOZ 2.1 benchmarks.