Boosting Few-Shot Text Classification via Distribution Estimation

no code implementations • 26 Mar 2023 • Han Liu, Feng Zhang, Xiaotong Zhang, Siyang Zhao, Fenglong Ma, Xiao-Ming Wu, Hongyang Chen, Hong Yu, Xianchao Zhang

Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain.

Few-Shot Image Classification Few-Shot Text Classification +1

Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection

no code implementations • 14 Jun 2022 • Han Liu, Feng Zhang, Xiaotong Zhang, Siyang Zhao, Junjie Sun, Hong Yu, Xianchao Zhang

Multi-label aspect category detection allows a given review sentence to contain multiple aspect categories, which is shown to be more practical in sentiment analysis and attracting increasing attention.

Aspect Category Detection Contrastive Learning +2

A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism

no code implementations • 5 Jun 2022 • Han Liu, Siyang Zhao, Xiaotong Zhang, Feng Zhang, Junjie Sun, Hong Yu, Xianchao Zhang

Zero-shot intent classification is a vital and challenging task in dialogue systems, which aims to deal with numerous fast-emerging unacquainted intents without annotated training data.

Classification intent-classification +4

An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling

no code implementations • Findings (EMNLP) 2021 • Han Liu, Feng Zhang, Xiaotong Zhang, Siyang Zhao, Xianchao Zhang

Intent classification (IC) and slot filling (SF) are critical building blocks in task-oriented dialogue systems.

Contrastive Learning intent-classification +5