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Multi-Label Hierarchical Text Classification (MLHTC) is the task of categorizing documents into one or more topics organized in an hierarchical taxonomy. MLHTC can be formulated by combining multiple binary classification problems with an independent classifier for each category. We propose a novel transfer learning based strategy, HTrans, where binary classifiers at lower levels in the hierarchy are initialized using parameters of the parent classifier and fine-tuned on the child categorydoi:10.18653/v1/p19-1633 dblp:conf/acl/BanerjeeAPT19 fatcat:3zrjlzcmjbbclf5ayuwbekxkc4