{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:21:52Z","timestamp":1762341712496},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2021\/550","type":"proceedings-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:00:49Z","timestamp":1628679649000},"page":"3992-3998","source":"Crossref","is-referenced-by-count":3,"title":["Cross-Domain Slot Filling as Machine Reading Comprehension"],"prefix":"10.24963","author":[{"given":"Mengshi","family":"Yu","sequence":"first","affiliation":[{"name":"Beijing Jiaotong University"}]},{"given":"Jian","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University"}]},{"given":"Yufeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University"}]},{"given":"Jinan","family":"Xu","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University"}]},{"given":"Yujie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University"}]}],"member":"10584","event":{"number":"30","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2021","name":"Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}","start":{"date-parts":[[2021,8,19]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2021,8,27]]}},"container-title":["Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:04:00Z","timestamp":1628679840000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2021\/550"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2021\/550","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]}}}