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    <abstract>This paper presents a schema-aware end-to-end neural network model for handling task-oriented dialogues based on a dynamic set of slots within a schema. Contrary to existing studies that proposed end-to-end approaches for task-oriented dialogue systems by relying on a unified schema across domains, we design our approach to support a domain covering multiple services where diverse schemas are available. To enable better generalizability among services and domains with different schemas, we supply the schema‘s context information including slot descriptions and value constraints to the model. The experimental results on a well-known Schema-Guided Dialogue (SGD) dataset demonstrated the performance improvement by the proposed model compared to state-of-the-art baselines in terms of end-to-end modeling, dialogue state tracking task, and generalization on new services and domains using a limited number of dialogues.</abstract>
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%0 Conference Proceedings
%T End-to-End Task-Oriented Dialogue Systems Based on Schema
%A Imrattanatrai, Wiradee
%A Fukuda, Ken
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F imrattanatrai-fukuda-2023-end
%X This paper presents a schema-aware end-to-end neural network model for handling task-oriented dialogues based on a dynamic set of slots within a schema. Contrary to existing studies that proposed end-to-end approaches for task-oriented dialogue systems by relying on a unified schema across domains, we design our approach to support a domain covering multiple services where diverse schemas are available. To enable better generalizability among services and domains with different schemas, we supply the schema‘s context information including slot descriptions and value constraints to the model. The experimental results on a well-known Schema-Guided Dialogue (SGD) dataset demonstrated the performance improvement by the proposed model compared to state-of-the-art baselines in terms of end-to-end modeling, dialogue state tracking task, and generalization on new services and domains using a limited number of dialogues.
%R 10.18653/v1/2023.findings-acl.645
%U https://aclanthology.org/2023.findings-acl.645/
%U https://doi.org/10.18653/v1/2023.findings-acl.645
%P 10148-10161

Markdown (Informal)

[End-to-End Task-Oriented Dialogue Systems Based on Schema](https://aclanthology.org/2023.findings-acl.645/) (Imrattanatrai & Fukuda, Findings 2023)

• End-to-End Task-Oriented Dialogue Systems Based on Schema (Imrattanatrai & Fukuda, Findings 2023)

ACL

• Wiradee Imrattanatrai and Ken Fukuda. 2023. End-to-End Task-Oriented Dialogue Systems Based on Schema. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10148–10161, Toronto, Canada. Association for Computational Linguistics.