mlsanityData to 7 Oct 2026

Many-Class Text Classification with Matching

arXiv 2205.11409 ↗First posted 23 May 2022cs.CLcited by 0

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Abstract (arXiv)

In this work, we formulate Text Classification as a Matching 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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 38 references that are in the map. First posted on arXiv 23 May 2022.

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References · 28

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16 more
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