Learning to Predict Charges for Criminal Cases with Legal Basis

Luo, Bingfeng; Feng, Yansong; Xu, Jianbo; Zhang, Xiang; Zhao, Dongyan

doi:10.18653/v1/d17-1289

Cited by 230 publications

(166 citation statements)

References 14 publications

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“…More sophisticated neural models have been considered only in Chinese. Luo et al (2017) use HANs to encode the facts of a case and a subset of predicted relevant law articles to predict crim-inal charges that have been manually annotated. In their experiments, the importance of few-shot learning is not taken into account since the criminal charges that appear fewer than 80 times are filtered out.…”

Section: Related Workmentioning

confidence: 99%

“…We also plan to apply neural methods to data from other courts, e.g., the European Court of Justice, the US Supreme Court, and multiple languages, to gain a broader perspective of their potential in legal justice prediction. Finally, we plan to adapt bespoke models proposed for the Chinese Criminal Court (Luo et al, 2017;Zhong et al, 2018;Hu et al, 2018) to data from other courts and explore multitask learning.…”

Section: Limitations and Future Workmentioning

confidence: 99%

See 1 more Smart Citation

Neural Legal Judgment Prediction in English

Chalkidis¹,

Androutsopoulos²,

Αλέτρας³

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

139

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Legal judgment prediction is the task of automatically predicting the outcome of a court case, given a text describing the case's facts. Previous work on using neural models for this task has focused on Chinese; only featurebased models (e.g., using bags of words and topics) have been considered in English. We release a new English legal judgment prediction dataset, containing cases from the European Court of Human Rights. We evaluate a broad variety of neural models on the new dataset, establishing strong baselines that surpass previous feature-based models in three tasks: (1) binary violation classification; (2) multi-label classification; (3) case importance prediction. We also explore if models are biased towards demographic information via data anonymization. As a side-product, we propose a hierarchical version of BERT, which bypasses BERT's length limitation. 1 The dataset is submitted at https://archive. org/details/ECHR-ACL2019.

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Section: Related Workmentioning

confidence: 99%

Section: Limitations and Future Workmentioning

confidence: 99%

Neural Legal Judgment Prediction in English

Chalkidis¹,

Androutsopoulos²,

Αλέτρας³

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

139

View full text Add to dashboard Cite

show abstract

“…SQA has found application in many fields, especially in the legal domain (Ye et al, 2018;Luo et al, 2017; and in high-school geography exams (Ding et al, 2018;. The latter is particularly challenging because a geographical scenario consists of both text and diagrams (e.g., maps, charts).…”

Section: Introductionmentioning

confidence: 99%

GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level

Huang

Shen

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Scenario-based question answering (SQA) has attracted increasing research attention. It typically requires retrieving and integrating knowledge from multiple sources, and applying general knowledge to a specific case described by a scenario. SQA widely exists in the medical, geography, and legal domainsboth in practice and in the exams. In this paper, we introduce the GeoSQA dataset. It consists of 1,981 scenarios and 4,110 multiplechoice questions in the geography domain at high school level, where diagrams (e.g., maps, charts) have been manually annotated with natural language descriptions to benefit NLP research. Benchmark results on a variety of state-of-the-art methods for question answering, textual entailment, and reading comprehension demonstrate the unique challenges presented by SQA for future research.

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“…Currently, researchers have done amounts of work on the field of Chinese legal instruments, involving a wide variety of research aspects. Law prediction [1,20] and charge prediction [8,13,25] have been widely studied, especially, CAIL2018 (Chinese AI and Law challenge, 2018) [22,26] was held to predict the judgment results of legal cases including relevant law articles, charges and prison terms. Some other researches include text summarization for legal documents [11], legal consultation [15,24] and legal entity identification [23].…”

Section: Introductionmentioning

confidence: 99%

CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension

Duan¹,

Wang²,

Wang³

et al. 2019

Lecture Notes in Computer Science

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We present a Chinese judicial reading comprehension (CJRC) dataset which contains approximately 10K documents and almost 50K questions with answers. The documents come from judgment documents and the questions are annotated by law experts. The CJRC dataset can help researchers extract elements by reading comprehension technology. Element extraction is an important task in the legal field. However, it is difficult to predefine the element types completely due to the diversity of document types and causes of action. By contrast, machine reading comprehension technology can quickly extract elements by answering various questions from the long document. We build two strong baseline models based on BERT and BiDAF. The experimental results show that there is enough space for improvement compared to human annotators.

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Learning to Predict Charges for Criminal Cases with Legal Basis

Cited by 230 publications

References 14 publications

Neural Legal Judgment Prediction in English

Neural Legal Judgment Prediction in English

GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level

CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension

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