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dc.contributor.authorXiao, Lin
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorJing, Liping
dc.contributor.authorHuang, Chi
dc.contributor.authorSong, Mingyang
dc.date.accessioned2021-02-04T11:39:08Z
dc.date.available2021-02-04T11:39:08Z
dc.date.issued2021-01-24
dc.identifier.urihttp://hdl.handle.net/10754/667224.1
dc.description.abstractMulti-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
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grant 61822601, 61773050, 61632004 and 61828302; The Beijing Natural Science Foundation under Grant Z180006; The National Key Research and Development Program of China under Grant 2020AAA0106800 and 2017YFC1703506; The Fundamental Research Funds for the Central Universities (2019JBZ110); And King Abdullah University of Science & Technology, under award number FCC/1/1976-19-01.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2101.09704
dc.rightsArchived with thanks to arXiv
dc.titleDoes Head Label Help for Long-Tailed Multi-Label Text Classification
dc.typePreprint
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.eprint.versionPre-print
dc.contributor.institutionBeijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China.
dc.identifier.arxivid2101.09704
kaust.personZhang, Xiangliang
kaust.grant.numberFCC/1/1976-19-01
refterms.dateFOA2021-02-04T11:39:47Z


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