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arXiv · 2106.10076

Label prompt for multi-label text classification

Rui Song·Xingbing Chen·Zelong Liu·Haining An·Zhiqi Zhang·Xiaoguang Wang·Hao Xu
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Abstract

One of the key problems in multi-label text classification is how to take advantage of the correlation among labels. However, it is very challenging to directly model the correlations among labels in a complex and unknown label space. In this paper, we propose a Label Mask multi-label text classification model (LM-MTC), which is inspired by the idea of cloze questions of language model. LM-MTC is able to capture implicit relationships among labels through the powerful ability of pre-train language models. On the basis, we assign a different token to each potential label, and randomly mask the token with a certain probability to build a label based Masked Language Model (MLM). We train the MTC and MLM together, further improving the generalization ability of the model. A large number of experiments on multiple datasets demonstrate the effectiveness of our method.

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Rui Song, Xingbing Chen, Zelong Liu, Haining An, Zhiqi Zhang, Xiaoguang Wang, Hao Xu. 2021-06-18. Label prompt for multi-label text classification. https://arxiv.org/abs/2106.10076

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