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arXiv · 2008.06695

Label-Wise Document Pre-Training for Multi-Label Text Classification

Han Liu·Caixia Yuan·Xiaojie Wang
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Abstract

A major challenge of multi-label text classification (MLTC) is to stimulatingly exploit possible label differences and label correlations. In this paper, we tackle this challenge by developing Label-Wise Pre-Training (LW-PT) method to get a document representation with label-aware information. The basic idea is that, a multi-label document can be represented as a combination of multiple label-wise representations, and that, correlated labels always cooccur in the same or similar documents. LW-PT implements this idea by constructing label-wise document classification tasks and trains label-wise document encoders. Finally, the pre-trained label-wise encoder is fine-tuned with the downstream MLTC task. Extensive experimental results validate that the proposed method has significant advantages over the previous state-of-the-art models and is able to discover reasonable label relationship. The code is released to facilitate other researchers.

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Han Liu, Caixia Yuan, Xiaojie Wang. 2020-08-15. Label-Wise Document Pre-Training for Multi-Label Text Classification. https://arxiv.org/abs/2008.06695

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