Schema Graph-Guided Prompt for Multi-Domain Dialogue State Tracking
- URL: http://arxiv.org/abs/2311.06345v1
- Date: Fri, 10 Nov 2023 19:00:02 GMT
- Title: Schema Graph-Guided Prompt for Multi-Domain Dialogue State Tracking
- Authors: Ruolin Su, Ting-Wei Wu and Biing-Hwang Juang
- Abstract summary: We propose a graph-based framework that learns domain-specific prompts by incorporating the dialogue schema.
Specifically, we embed domain-specific schema encoded by a graph neural network into the pre-trained language model.
Our experiments demonstrate that the proposed graph-based method outperforms other multi-domain DST approaches.
- Score: 16.955887768832046
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Tracking dialogue states is an essential topic in task-oriented dialogue
systems, which involve filling in the necessary information in pre-defined
slots corresponding to a schema. While general pre-trained language models have
been shown effective in slot-filling, their performance is limited when applied
to specific domains. We propose a graph-based framework that learns
domain-specific prompts by incorporating the dialogue schema. Specifically, we
embed domain-specific schema encoded by a graph neural network into the
pre-trained language model, which allows for relations in the schema to guide
the model for better adaptation to the specific domain. Our experiments
demonstrate that the proposed graph-based method outperforms other multi-domain
DST approaches while using similar or fewer trainable parameters. We also
conduct a comprehensive study of schema graph architectures, parameter usage,
and module ablation that demonstrate the effectiveness of our model on
multi-domain dialogue state tracking.
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