Label prompt for multi-label text classification (2106.10076v2)

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 LLM. LM-MTC is able to capture implicit relationships among labels through the powerful ability of pre-train LLMs. 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 LLM (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.