Does Head Label Help for Long-Tailed Multi-Label Text Classification

• URL: http://arxiv.org/abs/2101.09704v1
• Date: Sun, 24 Jan 2021 12:31:39 GMT
• Title: Does Head Label Help for Long-Tailed Multi-Label Text Classification
• Authors: Lin Xiao, Xiangliang Zhang, Liping Jing, Chi Huang, Mingyang Song
• Abstract summary: In real applications, the distribution of label frequency often exhibits a long tail, i.e., a few labels are associated with a large number of documents. We propose a Head-to-Tail Network (HTTN) to transfer the meta-knowledge from the data-rich head labels to data-poor tail labels.
• Score: 45.762555329467446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-label text classification (MLTC) aims to annotate documents with the most relevant labels from a number of candidate labels. In real applications, the distribution of label frequency often exhibits a long tail, i.e., a few labels are associated with a large number of documents (a.k.a. head labels), while a large fraction of labels are associated with a small number of documents (a.k.a. tail labels). To address the challenge of insufficient training data on tail label classification, we propose a Head-to-Tail Network (HTTN) to transfer the meta-knowledge from the data-rich head labels to data-poor tail labels. The meta-knowledge is the mapping from few-shot network parameters to many-shot network parameters, which aims to promote the generalizability of tail classifiers. Extensive experimental results on three benchmark datasets demonstrate that HTTN consistently outperforms the state-of-the-art methods. The code and hyper-parameter settings are released for reproducibility

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