BERT for Sequence-to-Sequence Multi-label Text Classification


Ramil Yarullin and Pavel Serdyukov


We study the BERT language representation model and the sequence generation model with BERT encoder for the multi-label text classification task. We show that the Sequence Generating BERT model achieves decent results in significantly fewer training epochs compared to the standard BERT. We also introduce and experimentally examine a mixed model, an ensemble of BERT and Sequence Generating BERT models. Our experiments demonstrate that the proposed model outperforms current baselines in several metrics on three well-studied multi-label classification datasets with English texts and two private Yandex Taxi datasets with Russian texts.

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