Many-Class Text Classification with Matching

• URL: http://arxiv.org/abs/2205.11409v1
• Date: Mon, 23 May 2022 15:51:19 GMT
• Title: Many-Class Text Classification with Matching
• Authors: Yi Song, Yuxian Gu, Minlie Huang
• Abstract summary: We formulate textbfText textbfClassification as a textbfMatching 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.
• Score: 65.74328417321738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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