@inproceedings{wu-etal-2022-towards,
title = "Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework",
author = "Wu, Yiquan and
Liu, Yifei and
Lu, Weiming and
Zhang, Yating and
Feng, Jun and
Sun, Changlong and
Wu, Fei and
Kuang, Kun",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.316",
doi = "10.18653/v1/2022.emnlp-main.316",
pages = "4787--4799",
abstract = "Legal judgment prediction (LJP) is a fundamental task in legal AI, which aims to assist the judge to hear the case and determine the judgment. The legal judgment usually consists of the law article, charge, and term of penalty. In the real trial scenario, the judge usually makes the decision step-by-step: first concludes the rationale according to the case{'}s facts and then determines the judgment. Recently, many models have been proposed and made tremendous progress in LJP, but most of them adopt an end-to-end manner that cannot be manually intervened by the judge for practical use. Moreover, existing models lack interpretability due to the neglect of rationale in the prediction process. Following the judge{'}s real trial logic, in this paper, we propose a novel Rationale-based Legal Judgment Prediction (RLJP) framework. In the RLJP framework, the LJP process is split into two steps. In the first phase, the model generates the rationales according to the fact description. Then it predicts the judgment based on the fact and the generated rationales. Extensive experiments on a real-world dataset show RLJP achieves the best results compared to the state-of-the-art models. Meanwhile, the proposed framework provides good interactivity and interpretability which enables practical use.",
}
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<abstract>Legal judgment prediction (LJP) is a fundamental task in legal AI, which aims to assist the judge to hear the case and determine the judgment. The legal judgment usually consists of the law article, charge, and term of penalty. In the real trial scenario, the judge usually makes the decision step-by-step: first concludes the rationale according to the case’s facts and then determines the judgment. Recently, many models have been proposed and made tremendous progress in LJP, but most of them adopt an end-to-end manner that cannot be manually intervened by the judge for practical use. Moreover, existing models lack interpretability due to the neglect of rationale in the prediction process. Following the judge’s real trial logic, in this paper, we propose a novel Rationale-based Legal Judgment Prediction (RLJP) framework. In the RLJP framework, the LJP process is split into two steps. In the first phase, the model generates the rationales according to the fact description. Then it predicts the judgment based on the fact and the generated rationales. Extensive experiments on a real-world dataset show RLJP achieves the best results compared to the state-of-the-art models. Meanwhile, the proposed framework provides good interactivity and interpretability which enables practical use.</abstract>
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%0 Conference Proceedings
%T Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework
%A Wu, Yiquan
%A Liu, Yifei
%A Lu, Weiming
%A Zhang, Yating
%A Feng, Jun
%A Sun, Changlong
%A Wu, Fei
%A Kuang, Kun
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F wu-etal-2022-towards
%X Legal judgment prediction (LJP) is a fundamental task in legal AI, which aims to assist the judge to hear the case and determine the judgment. The legal judgment usually consists of the law article, charge, and term of penalty. In the real trial scenario, the judge usually makes the decision step-by-step: first concludes the rationale according to the case’s facts and then determines the judgment. Recently, many models have been proposed and made tremendous progress in LJP, but most of them adopt an end-to-end manner that cannot be manually intervened by the judge for practical use. Moreover, existing models lack interpretability due to the neglect of rationale in the prediction process. Following the judge’s real trial logic, in this paper, we propose a novel Rationale-based Legal Judgment Prediction (RLJP) framework. In the RLJP framework, the LJP process is split into two steps. In the first phase, the model generates the rationales according to the fact description. Then it predicts the judgment based on the fact and the generated rationales. Extensive experiments on a real-world dataset show RLJP achieves the best results compared to the state-of-the-art models. Meanwhile, the proposed framework provides good interactivity and interpretability which enables practical use.
%R 10.18653/v1/2022.emnlp-main.316
%U https://aclanthology.org/2022.emnlp-main.316
%U https://doi.org/10.18653/v1/2022.emnlp-main.316
%P 4787-4799
Markdown (Informal)
[Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework](https://aclanthology.org/2022.emnlp-main.316) (Wu et al., EMNLP 2022)
ACL
- Yiquan Wu, Yifei Liu, Weiming Lu, Yating Zhang, Jun Feng, Changlong Sun, Fei Wu, and Kun Kuang. 2022. Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4787–4799, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.