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

Su Zhu, Jieyu Li, Lu Chen, Kai Yu


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 outperform strong baselines, and the previous state-of-the-art method (SOM-DST) can also be improved by our proposed schema graph.
Anthology ID:
2020.findings-emnlp.68
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
766–781
Language:
URL:
https://www.aclweb.org/anthology/2020.findings-emnlp.68
DOI:
10.18653/v1/2020.findings-emnlp.68
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.findings-emnlp.68.pdf