Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking
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.
Cite
@article{arxiv.2004.03386,
title = {Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking},
author = {Su Zhu and Jieyu Li and Lu Chen and Kai Yu},
journal= {arXiv preprint arXiv:2004.03386},
year = {2020}
}
Comments
16 pages, 4 figures, 11 tables. Accepted to EMNLP 2020 Findings