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Evidence Mining for Interpretable Charge Prediction via Prompt Learning | IEEE Journals & Magazine | IEEE Xplore

Evidence Mining for Interpretable Charge Prediction via Prompt Learning


Abstract:

Nowadays, more and more researchers are committed to applying artificial intelligence technology to the legal field to support decision-making. Charge prediction is a sub...Show More

Abstract:

Nowadays, more and more researchers are committed to applying artificial intelligence technology to the legal field to support decision-making. Charge prediction is a subtask of legal judgment prediction (LJP). Its purpose is to analyze the fact description of natural language text and predict a charge corresponding to a case. At present, most of the research takes charge prediction as a multiclass classification task, which leads to unsatisfactory interpretation due to the weak semantic correlation between the fact descriptions and charge labels. In order to solve this problem, we propose a method of generative evidence mining based on prompt learning. Specifically, in the training phase, we reformulate the charge labels into the prompt template that we design to enhance the semantic correlation between the charge labels and the fact descriptions. In the testing phase, the charge labels are generated via the model based on prompt learning. Meanwhile, we calculate the attention score of each sentence from the multihead self-attention in the transformer encoder and choose the sentence with the highest attention score as the evidence. Our experimental results on a real dataset show that our method is better than the traditional fine-tuning-based classification method.
Published in: IEEE Transactions on Computational Social Systems ( Volume: 11, Issue: 4, August 2024)
Page(s): 4556 - 4566
Date of Publication: 08 June 2022

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