  Legal Judgment Prediction (LJP) aims to automatically predict judgment
results, such as charges, relevant law articles, and the term of penalty. It
plays a vital role in legal assistant systems and has become a popular research
topic in recent years. This paper concerns a worthwhile but not well-studied
LJP task, Appeal judgment Prediction (AJP), which predicts the judgment of an
appellate court on an appeal case based on the textual description of case
facts and grounds of appeal. There are two significant challenges in practice
to solve the AJP task. One is how to model the appeal judgment procedure
appropriately. The other is how to improve the interpretability of the
prediction results. We propose a Sequential Multi-task Learning Framework with
Task Dependency for Appeal Judgement Prediction (SMAJudge) to address these
challenges. SMAJudge utilizes two sequential components to model the complete
proceeding from the lower court to the appellate court and employs an attention
mechanism to make the prediction more explainable, which handles the challenges
of AJP effectively. Experimental results obtained with a dataset consisting of
more than 30K appeal judgment documents have revealed the effectiveness and
superiority of SMAJudge.
