Zero-shot text classification via self-supervised tuning

• Main Authors: Liu, Chaoqun, Zhang, Wenxuan, Chen, Guizhen, Wu, Xiaobao, Luu, Anh Tuan, Chang, Chip Hong, Bing, Lidong
• Other Authors: Interdisciplinary Graduate School (IGS)
• Format: Conference or Workshop Item
• Language: English
• Published: 2023
• Subjects: Computer Science - Computation and Language; Computer Science - Artificial Intelligence; Engineering::Computer science and engineering::Computing methodologies::Document and text processing; Zero-Shot Text Classification; Self-Supervised Tuning
• Online Access: https://hdl.handle.net/10356/168505; https://2023.aclweb.org/
• Institution: Nanyang Technological University

Existing solutions to zero-shot text classification either conduct prompting with pre-trained language models, which is sensitive to the choices of templates, or rely on large-scale annotated data of relevant tasks for meta-tuning. In this work, we propose a new paradigm based on self-supervised...