  In this work, we formulate \textbf{T}ext \textbf{C}lassification as a
\textbf{M}atching problem between the text and the labels, and propose a simple
yet effective framework named TCM. Compared with previous text classification
approaches, TCM takes advantage of the fine-grained semantic information of the
classification labels, which helps distinguish each class better when the class
number is large, especially in low-resource scenarios. TCM is also easy to
implement and is compatible with various large pretrained language models. We
evaluate TCM on 4 text classification datasets (each with 20+ labels) in both
few-shot and full-data settings, and this model demonstrates significant
improvements over other text classification paradigms. We also conduct
extensive experiments with different variants of TCM and discuss the underlying
factors of its success. Our method and analyses offer a new perspective on text
classification.

Computation and Language