Abstract
Charge prediction, determining charges for cases by analyzing the textual fact descriptions, is a fundamental technology in legal information retrieval systems. In practice, the fact descriptions could exhibit a significant intra-class variation due to factors like non-normative use of language by different users, which makes the prediction task very challenging, especially for charge classes with too few samples to cover the expression variation. In this work, we explore to use the charge (label) definitions to alleviate this issue. The key idea is that the expressions in a fact description should have corresponding formal terms in label definitions, and those terms are shared across classes and could account for the diversity in the fact descriptions. Thus, we propose to create auxiliary fact representations from charge definitions to augment fact descriptions representation. Specifically, we design label definitions augmented interaction model, where fact description interacts with the relevant charge definitions and terms in those definitions by a sentence- and word-level attention scheme, to generated auxiliary representations. Experimental results on two datasets show that our model achieves significant improvement than baselines, especially for dataset with few samples.
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Notes
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In CAIL2018 dataset, CAIL150k is ./exercise_contest/data_train.json. CAIL30k is ./final_test.json. They share the same validation and test set (./exercise_contest/data_valid.json and data_test.json).
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Acknowledgments
This work was supported by National Key R&D Program of China (2018YFC0831302), National Natural Sciences Foundation of China (61972386), and Youth Innovation Promotion Association at Chinese Academy of Sciences.
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Kang, L., Liu, J., Liu, L., Ye, D. (2021). Label Definitions Augmented Interaction Model for Legal Charge Prediction. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12656. Springer, Cham. https://doi.org/10.1007/978-3-030-72113-8_18
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