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Standard methods for multi-label text classification largely rely onencoder-only pre-trained language models, whereas encoder-decoder models haveproven more effective in other classification tasks. In this study, we comparefour methods for multi-label classification, two based on an encoder only, andtwo 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 levelsof 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 finergranularity. Using encoder-decoder models in a non-autoregressive fashion, inparticular, yields the best performance overall, so we further study thisapproach through ablations to better understand its strengths.

An Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text