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.
@article{arxiv.2106.10076,
title = {Label prompt for multi-label text classification},
author = {Rui Song and Xingbing Chen and Zelong Liu and Haining An and Zhiqi Zhang and Xiaoguang Wang and Hao Xu},
journal= {arXiv preprint arXiv:2106.10076},
year = {2023}
}