Abstract：The legal field is using more artificial intelligence methods such as legal judgment prediction (LJP) based on case description texts using natural language processing. Charge prediction and law article recommendations are two important LJP sub-tasks that are closely related and interact with each other. However, previous studies have usually analyzed them as two independent tasks that are analyzed separately. Furthermore, charge prediction and law article recommendations both face the problem of confusing charges. To this end, this paper presents a multi-task learning model for joint modeling of charge prediction and law article recommendations. Confusing charges are handled by using a set of charge keywords extracted from case description texts using statistical techniques for integration into the multi-task learning model. This method was evaluated using the CAIL2018 legal dataset. The results show that incorporating the charge keywords into the multi-task learning model effectively resolves the confusing charge problem and significantly improves both the charge prediction and the law article recommendation results.
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