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Label-Embedding Bi-directional Attentive Model for Multi-label Text Classification

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Abstract

Multi-label text classification is a critical task in natural language processing field. As the latest language representation model, BERT obtains new state-of-the-art results in the classification task. Nevertheless, the text classification framework of BERT neglects to make full use of the token-level text representation and label embedding, since it only utilizes the final hidden state corresponding to CLS token as sequence-level text representation for classification. We assume that the finer-grained token-level text representation and label embedding contribute to classification. Consequently, in this paper, we propose a Label-Embedding Bi-directional Attentive model to improve the performance of BERT’s text classification framework. In particular, we extend BERT’s text classification framework with label embedding and bi-directional attention. Experimental results on the five datasets indicate that our model has notable improvements over both baselines and state-of-the-art models.

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Notes

  1. https://slashdot.org/.

  2. http://www.daviddlewis.com/resources/testcollections/reuters21578/.

  3. https://github.com/google-research/bert.

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Acknowledgements

This work is partially supported by National Natural Science Foundation of China (No. U1711263).

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Authors and Affiliations

  1. School of Data and Computer Science, Guangdong Province Key Lab of Computational Science, Sun Yat-Sen University, Guangdong, 510275, People’s Republic of China

    Naiyin Liu, Qianlong Wang & Jiangtao Ren

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  1. Naiyin Liu
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  2. Qianlong Wang
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  3. Jiangtao Ren
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Correspondence to Jiangtao Ren.

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Liu, N., Wang, Q. & Ren, J. Label-Embedding Bi-directional Attentive Model for Multi-label Text Classification. Neural Process Lett 53, 375–389 (2021). https://doi.org/10.1007/s11063-020-10411-8

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