{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/knowledge-driven-slot-constraints-for-goal","title":"Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems","arxiv_id":null,"date":"2021-06-01","proceeding":"NAACL 2021 4","authors":["Piyawat Lertvittayakumjorn","Daniele Bonadiman","Saab Mansour"],"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.","url_abs":"https://aclanthology.org/2021.naacl-main.266","url_pdf":"https://aclanthology.org/2021.naacl-main.266.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"knowledge-driven-slot-constraints-for-goal","repo_url":"https://github.com/amazon-research/nlu-slot-constraints","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"goal-oriented-dialogue-systems","task_name":"Goal-Oriented Dialogue Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}