{"type":"ScholarlyArticle","title":"Cross-Domain Slot Filling as Machine Reading Comprehension","authors":[{"name":"Mengshi Yu","slug":"mengshi-yu","given":"Mengshi","family":"Yu","affiliation":""},{"name":"Jian Liu","slug":"jian-liu","given":"Jian","family":"Liu","affiliation":""},{"name":"Yufeng Chen","slug":"yufeng-chen","given":"Yufeng","family":"Chen","affiliation":""},{"name":"Jinan Xu","slug":"jinan-xu","given":"Jinan","family":"Xu","affiliation":""},{"name":"Yujie Zhang","slug":"yujie-zhang","given":"Yujie","family":"Zhang","affiliation":""}],"abstract":"With task-oriented dialogue systems being widely applied in everyday life, slot filling, the essential component of task-oriented dialogue systems, is required to be quickly adapted to new domains that contain domain-specific slots with few or no training data. Previous methods for slot filling usually adopt sequence labeling framework, which, however, often has limited ability when dealing with the domain-specific slots. In this paper, we take a new perspective on cross-domain slot filling by framing it as a machine reading comprehension (MRC) problem. Our approach firstly transforms slot names into well-designed queries, which contain rich informative prior knowledge and are very helpful for the detection of domain-specific slots. In addition, we utilize the large-scale MRC dataset for pre-training, which further alleviates the data scarcity problem. Experimental results on SNIPS and ATIS datasets show that our approach consistently outperforms the existing state-of-the-art methods by a large margin.","doi":"10.24963/ijcai.2021/550","url":"https://ijcai.org/proceedings/2021/550","pdf":"https://ijcai.org/proceedings/2021/0550.pdf","markdown":"https://ijcai.org/proceedings/2021/550.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/550","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Natural Language Processing","track":"Main Track","number":550,"pages":{"first":3992,"last":3998},"keywords":["Natural Language Processing: Dialogue","Natural Language Processing: Information Extraction"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}