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| Judgment prediction is a difficult problem in judicial field. Given a fact description, judge need to conduct several documents to decide the related articles. This task is complex and requires a lot of energy. Previous works always treat this task as a multi-label learning paradigm for judgment prediction. These work usually neglect the external knowledge, thus the performance may be limited. In this paper, this topic use a multi-task learning framework with pretrained external knowledge to address this issues, and this topic propose a BERT based multi-task learning model(BMM for short). Specially, BMM use a pretrained BERT model to obtain external knowledge, then a multi-task learning framework is incorporated to learn multi-label classification and language model jointly. Experimental results on three real-world datasets demonstrate that the proposed model achieves significant improvements over state-of-the-art methods. |
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| Keywords:Judgment Prediction; Multi-task learning; Multi-label classification |
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