@inproceedings{kementchedjhieva-chalkidis-2023-exploration,
    title = "An Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text",
    author = "Kementchedjhieva, Yova  and
      Chalkidis, Ilias",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.360",
    doi = "10.18653/v1/2023.findings-acl.360",
    pages = "5828--5843",
    abstract = "Standard methods for multi-label text classification largely rely on encoder-only pre-trained language models, whereas encoder-decoder models have proven more effective in other classification tasks. In this study, we compare four methods for multi-label classification, two based on an encoder only, and two based on an encoder-decoder. We carry out experiments on four datasets{---}two in the legal domain and two in the biomedical domain, each with two levels of label granularity{---} and always depart from the same pre-trained model, T5. Our results show that encoder-decoder methods outperform encoder-only methods, with a growing advantage on more complex datasets and labeling schemes of finer granularity. Using encoder-decoder models in a non-autoregressive fashion, in particular, yields the best performance overall, so we further study this approach through ablations to better understand its strengths.",
}

Standard methods for multi-label text classification largely rely on encoder-only pre-trained language models, whereas encoder-decoder models have proven more effective in other classification tasks. In this study, we compare four methods for multi-label classification, two based on an encoder only, and two based on an encoder-decoder. We carry out experiments on four datasets{---}two in the legal domain and two in the biomedical domain, each with two levels of label granularity{---} and always depart from the same pre-trained model, T5. Our results show that encoder-decoder methods outperform encoder-only methods, with a growing advantage on more complex datasets and labeling schemes of finer granularity. Using encoder-decoder models in a non-autoregressive fashion, in particular, yields the best performance overall, so we further study this approach through ablations to better understand its strengths.
Kementchedjhieva, Yova Chalkidis, Ilias
An Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text