https://scholars.lib.ntu.edu.tw/handle/123456789/581488
標題: | Slot-gated modeling for joint slot filling and intent prediction | 作者: | Goo C.-W Gao G Hsu Y.-K Huo C.-L Chen T.-C Hsu K.-W Chen Y.-N. YUN-NUNG CHEN |
關鍵字: | Computational linguistics; Global optimization; Semantics; Intent detection; Recurrent neural network model; Semantic frames; Sentence level; State-of-the-art performance; Recurrent neural networks | 公開日期: | 2018 | 卷: | 2 | 起(迄)頁: | 753-757 | 來源出版物: | NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference | 摘要: | Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights. Considering that slot and intent have the strong relationship, this paper proposes a slot gate that focuses on learning the relationship between intent and slot attention vectors in order to obtain better semantic frame results by the global optimization. The experiments show that our proposed model significantly improves sentence-level semantic frame accuracy with 4.2% and 1.9% relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively. ? 2018 Association for Computational Linguistics. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057752937&partnerID=40&md5=b0765c60c2a7bd01e9410ee65e60a209 https://scholars.lib.ntu.edu.tw/handle/123456789/581488 |
顯示於: | 資訊工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。