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

2021-01-24 12:31:39
Lin Xiao, Xiangliang Zhang, Liping Jing, Chi Huang, Mingyang Song

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

Abstract (translated)

URL

https://arxiv.org/abs/2101.09704

PDF

https://arxiv.org/pdf/2101.09704.pdf


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