  Confusing charge prediction is a challenging task in legal AI, which involves
predicting confusing charges based on fact descriptions. While existing charge
prediction methods have shown impressive performance, they face significant
challenges when dealing with confusing charges, such as Snatch and Robbery. In
the legal domain, constituent elements play a pivotal role in distinguishing
confusing charges. Constituent elements are fundamental behaviors underlying
criminal punishment and have subtle distinctions among charges. In this paper,
we introduce a novel From Graph to Word Bag (FWGB) approach, which introduces
domain knowledge regarding constituent elements to guide the model in making
judgments on confusing charges, much like a judge's reasoning process.
Specifically, we first construct a legal knowledge graph containing constituent
elements to help select keywords for each charge, forming a word bag.
Subsequently, to guide the model's attention towards the differentiating
information for each charge within the context, we expand the attention
mechanism and introduce a new loss function with attention supervision through
words in the word bag. We construct the confusing charges dataset from
real-world judicial documents. Experiments demonstrate the effectiveness of our
method, especially in maintaining exceptional performance in imbalanced label
distributions.
