Causal representation for few-shot text classification.

Few-Shot Text Classification (FSTC) is a fundamental natural language processing problem that aims to classify small amounts of text with high accuracy. Mainstream methods model the superficial statistical relationships between text and labels. However, distributional imbalance problems are encountered during few-shot learning; therefore, questions remain regarding its robustness and generalization. The above problems can be addressed by intrinsic causal mechanisms. We introduce a general structural causal model to formalize the FSTC problem. To extract causal associations from text and reconstruct information to achieve a better classification effect, we propose a causal representation for few-shot learning (CRFL) framework to force representations to be causally related. Our framework performs well when the number of training examples is small or when it generalizes to the data transfer situation. CRFL is orthogonal to the existing fine-tuning and few-shot meta-learning methods and can be applied to any task. Extensive experimental results obtained on several widely used datasets validate the effectiveness of our approach, which can be attributed to our model's stability and logical reasoning. [ABSTRACT FROM AUTHOR]