会议论文

Wang, Yufan

英文

2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)

2639-1589

2019

960-967

IEEE International Conference on Big Data (Big Data)

IEEE International Conference on Big Data

DEC 09-12, 2019

Los Angeles, CA

[Wang, Yufan] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Hubei Prov Key Lab Artificial Intelligence & Smar, Natl Language Resources Monitor & Res Ctr Network, Wuhan, Peoples R China.^[He, Tingting;Fan, Rui;Zhou, Wenji;Tu, Xinhui] Cent China Normal Univ, Sch Comp, Hubei Prov Key Lab Artificial Intelligence & Smar, Natl Language Resources Monitor & Res Ctr Network, Wuhan, Peoples R China.

IEEE Comp Soc, IEEE, Baidu, Very, Ankura

Baru, C Huan, J Khan, L Hu, XH Ak, R Tian, Y Barga, R Zaniolo, C Lee, K Ye, YF

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

978-1-7281-0858-2

Fundamental Research Funds for Central UniversitiesFundamental Research Funds for the Central Universities [CCNU18JCK05]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61532008]; National Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61572223]; National Key Research and Development Program of China [2017YFC0909502]

本校为第一且通讯机构

At present, spoken language understanding (SLU) in multi-turn dialogue is a research hotspot, which mainly includes intent detection and slot filling. SLU models trained by large-scale corpus can learn good superficial semantic and grammatical information. But they lack the ability for modeling the knowledge needed to understand language. In order to further understand the deep semantic information of the dialogue, external knowledge needs to be modeled and incorporated into the SLU model. In addition, utilizing the correlation between history dialogue and current utterance is able to understa...