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-12.4
Paper Title GAN-BASED OUT-OF-DOMAIN DETECTION USING BOTH IN-DOMAIN AND OUT-OF-DOMAIN SAMPLES
Authors Chaojie Liang, Peijie Huang, Wenbin Lai, Ziheng Ruan, South China Agricultural University, China
SessionHLT-12: Language Understanding 4: Semantic Understanding
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 In domain classification for spoken language understanding, correct detection of out-of-domain (OOD) utterances is crucial because it reduces confusion and unnecessary interaction costs between users and the systems. In the situation where both in-domain (ID) and OOD samples are available, our goal is to take advantage of OOD samples under the GAN-based framework for OOD detection. We propose a GAN-based OOD detector with OOD prior distribution and weighted loss (WOODP-GAN). The model consists of a GAN-based detector with OOD prior distribution for generating effective pseudo OOD samples, and a weighted loss function for balancing the loss of fake OOD samples against real OOD samples in the discriminator. Extensive experiments show our proposed WOODP-GAN model outperforms the existing methods in the benchmark dataset CLINC150.