Automatic Charge Identification (ACI) is the task of identifying the relevant legal charges given the facts of a situation and the statutory laws that define these charges, and is a crucial aspect of the judicial process. Prior works focus on learning charge-side representations by modeling relationships between the charges, but not much effort has been made in improving fact-side representations. We observe that only a small fraction of sentences in the facts actually indicates the charges. We show that by using a very small subset (＜ 3%) of fact descriptions annotated with sentence-level charges, we can achieve an improvement across a range of different ACI models, as compared to modeling just the main document-level task on a much larger dataset. Additionally, we propose a novel model that utilizes sentence-level charge labels as an auxiliary task, coupled with the main task of document-level charge identification in a multi-task learning framework. The proposed model comprehensively outperforms a large number of recent baseline models for ACI. The improvement in performance is particularly noticeable for the rare charges which are known to be especially challenging to identify.
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