Improving Statute Prediction via Mining Correlations between Statutes

Yi Feng, Chuanyi Li, Jidong Ge, Bin Luo
Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:710-725, 2019.

Abstract

The task of statute prediction focuses on determining applicable statutes for legal cases with the inputs of fact descriptions, which is crucial for both legal experts and ordinary people without professional knowledge. Existing works just consider the correspondence from facts to individual statutes and ignore the correlations between statutes. Moreover, charges of cases have associations with statutes. To address these issues, we formulate statute prediction task as a sequence generation problem and propose a novel joint generative model to mine correlations between statutes. By integrating statute prediction task and charge prediction task, we also make model learn associations between statutes and charges. Experiments show our model outperforms several baselines significantly and correlative statutes are predicted accurately.

Cite this Paper

BibTeX
@InProceedings{pmlr-v101-feng19a, title = {Improving Statute Prediction via Mining Correlations between Statutes}, author = {Feng, Yi and Li, Chuanyi and Ge, Jidong and Luo, Bin}, booktitle = {Proceedings of The Eleventh Asian Conference on Machine Learning}, pages = {710--725}, year = {2019}, editor = {Lee, Wee Sun and Suzuki, Taiji}, volume = {101}, series = {Proceedings of Machine Learning Research}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v101/feng19a/feng19a.pdf}, url = {https://proceedings.mlr.press/v101/feng19a.html}, abstract = {The task of statute prediction focuses on determining applicable statutes for legal cases with the inputs of fact descriptions, which is crucial for both legal experts and ordinary people without professional knowledge. Existing works just consider the correspondence from facts to individual statutes and ignore the correlations between statutes. Moreover, charges of cases have associations with statutes. To address these issues, we formulate statute prediction task as a sequence generation problem and propose a novel joint generative model to mine correlations between statutes. By integrating statute prediction task and charge prediction task, we also make model learn associations between statutes and charges. Experiments show our model outperforms several baselines significantly and correlative statutes are predicted accurately.} }
Endnote
%0 Conference Paper %T Improving Statute Prediction via Mining Correlations between Statutes %A Yi Feng %A Chuanyi Li %A Jidong Ge %A Bin Luo %B Proceedings of The Eleventh Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Wee Sun Lee %E Taiji Suzuki %F pmlr-v101-feng19a %I PMLR %P 710--725 %U https://proceedings.mlr.press/v101/feng19a.html %V 101 %X The task of statute prediction focuses on determining applicable statutes for legal cases with the inputs of fact descriptions, which is crucial for both legal experts and ordinary people without professional knowledge. Existing works just consider the correspondence from facts to individual statutes and ignore the correlations between statutes. Moreover, charges of cases have associations with statutes. To address these issues, we formulate statute prediction task as a sequence generation problem and propose a novel joint generative model to mine correlations between statutes. By integrating statute prediction task and charge prediction task, we also make model learn associations between statutes and charges. Experiments show our model outperforms several baselines significantly and correlative statutes are predicted accurately.
APA
Feng, Y., Li, C., Ge, J. & Luo, B.. (2019). Improving Statute Prediction via Mining Correlations between Statutes. Proceedings of The Eleventh Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 101:710-725 Available from https://proceedings.mlr.press/v101/feng19a.html.

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