Are We Really Making Much Progress in Text Classification? A Comparative Review

• URL: http://arxiv.org/abs/2204.03954v5
• Date: Sun, 4 Jun 2023 17:40:32 GMT
• Title: Are We Really Making Much Progress in Text Classification? A Comparative Review
• Authors: Lukas Galke, Andor Diera, Bao Xin Lin, Bhakti Khera, Tim Meuser, Tushar Singhal, Fabian Karl, Ansgar Scherp
• Abstract summary: This study reviews and compares methods for single-label and multi-label text classification. Results reveal that all recently proposed graph-based and hierarchy-based methods fail to outperform pre-trained language models.
• Score: 2.579878570919875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study reviews and compares methods for single-label and multi-label text classification, categorized into bag-of-words, sequence-based, graph-based, and hierarchical methods. The comparison aggregates results from the literature over five single-label and seven multi-label datasets and complements them with new experiments. The findings reveal that all recently proposed graph-based and hierarchy-based methods fail to outperform pre-trained language models and sometimes perform worse than standard machine learning methods like a multilayer perceptron on a bag-of-words. To assess the true scientific progress in text classification, future work should thoroughly test against strong bag-of-words baselines and state-of-the-art pre-trained language models.

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