2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDHLT-11.5
Paper Title Improving Cross-domain Slot Filling with Common Syntactic Structure
Authors Luchen Liu, Xixun Lin, Peng Zhang, Chinese Academy of Sciences, China; Bin Wang, Xiaomi Inc., China
SessionHLT-11: Language Understanding 3: Speech Understanding - General Topics
LocationGather.Town
Session Time:Thursday, 10 June, 13:00 - 13:45
Presentation Time:Thursday, 10 June, 13:00 - 13:45
Presentation Poster
Topic Human Language Technology: [HLT-UNDE] Spoken Language Understanding and Computational Semantics
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Cross-domain slot filling is a challenging task in spoken language understanding due to the differences in text genre across domains. In this paper, we attempt to solve this task by exploiting the syntactic structures of user utterances, because these syntactic structures are actually accessible and can be shared between utterances from different domains. To this end, we propose a novel Syntactic Structure Encoder (SSE) module and incorporate it into a detection-prediction framework. SSE introduces graph convolutional network (GCN) to learn the common structures from multiple source domains, which are helpful to better adaptation on the target domain. Experimental results conducted on SNIPS dataset show that our model significantly outperforms the state-of-the-art approach in cross-domain slot filling. Specifically, our model outperforms the best model by ~4% and ~5% F1-scores under the 20-example and 50-example settings, respectively.