Ziheng Ruan — accepted AI-conference papers

2024

Anchor-Guided GAN with Contrastive Loss for Low-Resource Out-of-Domain Detection

Jiankai Zhu, Peijie Huang, Ziheng Ruan, Yuhui Zhu, Chaojie Liang, Yuhong Xu

ICASSP 2024accepted

Out-of-domain (OOD) detection plays an important role in spoken language understanding (SLU). It can help dialog systems reduce confusion between in-domain (ID) and OOD utterances. Many dialog systems train their model to achieve this goal by collecting annotated OOD and ID data. However, acquiring…

Cited by 0 Source ↗Scholar ↗

2021

GAN-Based Out-of-Domain Detection Using Both In-Domain and Out-of-Domain Samples

Chaojie Liang, Peijie Huang, Wenbin Lai, Ziheng Ruan

ICASSP 2021accepted

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…

Cited by 0 Source ↗Scholar ↗