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        <title>Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems</title>
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        <namePart type="given">Piyawat</namePart>
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    <abstract>In goal-oriented dialogue systems, users provide information through slot values to achieve specific goals. Practically, some combinations of slot values can be invalid according to external knowledge. For example, a combination of “cheese pizza” (a menu item) and “oreo cookies” (a topping) from an input utterance “Can I order a cheese pizza with oreo cookies on top?” exemplifies such invalid combinations according to the menu of a restaurant business. Traditional dialogue systems allow execution of validation rules as a post-processing step after slots have been filled which can lead to error accumulation. In this paper, we formalize knowledge-driven slot constraints and present a new task of constraint violation detection accompanied with benchmarking data. Then, we propose methods to integrate the external knowledge into the system and model constraint violation detection as an end-to-end classification task and compare it to the traditional rule-based pipeline approach. Experiments on two domains of the MultiDoGO dataset reveal challenges of constraint violation detection and sets the stage for future work and improvements.</abstract>
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    <identifier type="doi">10.18653/v1/2021.naacl-main.266</identifier>
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%0 Conference Proceedings
%T Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems
%A Lertvittayakumjorn, Piyawat
%A Bonadiman, Daniele
%A Mansour, Saab
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F lertvittayakumjorn-etal-2021-knowledge
%X In goal-oriented dialogue systems, users provide information through slot values to achieve specific goals. Practically, some combinations of slot values can be invalid according to external knowledge. For example, a combination of “cheese pizza” (a menu item) and “oreo cookies” (a topping) from an input utterance “Can I order a cheese pizza with oreo cookies on top?” exemplifies such invalid combinations according to the menu of a restaurant business. Traditional dialogue systems allow execution of validation rules as a post-processing step after slots have been filled which can lead to error accumulation. In this paper, we formalize knowledge-driven slot constraints and present a new task of constraint violation detection accompanied with benchmarking data. Then, we propose methods to integrate the external knowledge into the system and model constraint violation detection as an end-to-end classification task and compare it to the traditional rule-based pipeline approach. Experiments on two domains of the MultiDoGO dataset reveal challenges of constraint violation detection and sets the stage for future work and improvements.
%R 10.18653/v1/2021.naacl-main.266
%U https://aclanthology.org/2021.naacl-main.266
%U https://doi.org/10.18653/v1/2021.naacl-main.266
%P 3407-3419

Markdown (Informal)

[Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems](https://aclanthology.org/2021.naacl-main.266) (Lertvittayakumjorn et al., NAACL 2021)

• Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems (Lertvittayakumjorn et al., NAACL 2021)

ACL

• Piyawat Lertvittayakumjorn, Daniele Bonadiman, and Saab Mansour. 2021. Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3407–3419, Online. Association for Computational Linguistics.