GUDN: A novel guide network with label reinforcement strategy for extreme multi-label text classification

Extreme multi-label text classification (XMTC) is an emerging and essential task in natural language processing. Its objective is to retrieve the most relevant labels for a text from a large set of labels while balancing time and accuracy. Although large-scale pre-trained models have brought new perspectives to this task, more attention should be given to valuable fine-tuned methods and the significant semantic gap between texts and labels. In this paper, we propose a novel guide network (GUDN) with a label reinforcement strategy based on label semantics to help fine-tune pre-trained models for classification. Experimental results demonstrate that GUDN outperforms state-of-the-art methods on Eurlex-4k and achieves competitive results on other popular datasets. In addition, we find that meaningless tokens can harm the Transformer-based model’s classification accuracy in another experiment. We conclude that GUDN is effective in the presence of solid semantics. Our source code is available at https://t.hk.uy/aFSH.