Charge-Based Prison Term Prediction with Deep Gating Network

Chen, Huajie; Cai, Deng; Dai, Wei; Dai, Zehui; Ding, Yadong

doi:10.18653/v1/d19-1667

Cited by 60 publications

(27 citation statements)

References 22 publications

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“…Firstly, the CASELAW4 dataset could be used in a number of prediction models, from more complex LSTMs to transformers to pre-trained language models. Since the problem of legal outcome prediction is a highly complex problem that relies on numerous factors, sophisticated deep learning models show promising results [12,19,25]. Secondly, it is important to further improve outcome extraction, to go beyond the binary system of AFFIRM and REVERSE labels and to move to more granular MIXED cases.…”

Section: Discussionmentioning

confidence: 99%

“…Previous works focused mostly on European and Chinese law. They include predicting outcomes in the French Supreme Court [18], in the European Court of Justice [14,19], and in the European Court of Human Rights [13,12,15,16], as well as predicting outcomes of criminal cases from the Supreme People's Court of China [10,20,21,22,23,24,25]. However, very limited work focused on the U.S. and U.K. law systems [9,17], and to our knowledge, no attempt has yet been made to predict outcomes for cases from the CAP dataset [8].…”

Section: Legal Outcome Predictionmentioning

confidence: 99%

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Extracting Outcomes from Appellate Decisions in US State Courts

Petrova

Armour

Lukasiewicz

2020

Frontiers in Artificial Intelligence and Applications

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Predicting the outcome of a legal process has recently gained considerable research attention. Numerous attempts have been made to predict the exact outcome, judgment, charge, and fines of a case given the textual description of its facts and metadata. However, most of the effort has been focused on Chinese and European law, for which there exist annotated datasets. In this paper, we introduce CASELAW4 — a new dataset of 350k common law judicial decisions from the U.S. Caselaw Access Project, of which 250k have been automatically annotated with binary outcome labels of AFFIRM or REVERSE by our hybrid learning system. To our knowledge, it is the first attempt to perform outcome extraction (a) on such a large volume of English-language judicial opinions, (b) on the Caselaw Access Project data, and (c) on US State Courts of Appeal cases, and it paves the way to large-scale outcome prediction and advanced legal analytics using U.S. Case Law. We set up baseline results for the outcome extraction task on the new dataset, achieving an F-measure of 82.32%.

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Section: Discussionmentioning

confidence: 99%

Section: Legal Outcome Predictionmentioning

confidence: 99%

Extracting Outcomes from Appellate Decisions in US State Courts

Petrova

Armour

Lukasiewicz

2020

Frontiers in Artificial Intelligence and Applications

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“…For the parameters of BERT, we use the pretrained parameters on Chinese criminal cases (Zhong et al, 2019b). Secondly, we implement several models which are specially designed for LJP, including FactLaw (Luo et al, 2017), TopJudge , and Gating Network (Chen et al, 2019). The results can be found in Table 4.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Several new LegalAI datasets have been proposed (Kano et al, 2018;Duan et al, 2019;Chalkidis et al, 2019b,a), which can serve as benchmarks for research in the field. Based on these datasets, researchers began exploring NLP-based solutions to a variety of LegalAI tasks, such as Legal Judgment Prediction (Aletras et al, 2016;Luo et al, 2017;Chen et al, 2019), Court View Generation (Ye et al, 2018), Legal Entity Recognition and Classification (Cardellino et al, 2017;ANGELIDIS et al, 2018), Legal Question Answering (Monroy et al, 2009;Taniguchi and Kano, 2016;Kim and Goebel, 2017), Legal Summarization (Hachey and Grover, 2006;Bhattacharya et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence

Zhong¹,

Xiao²,

Tu³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

193

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Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial intelligence, especially natural language processing, to benefit tasks in the legal domain. In recent years, LegalAI has drawn increasing attention rapidly from both AI researchers and legal professionals, as LegalAI is beneficial to the legal system for liberating legal professionals from a maze of paperwork. Legal professionals often think about how to solve tasks from rulebased and symbol-based methods, while NLP researchers concentrate more on data-driven and embedding methods. In this paper, we describe the history, the current state, and the future directions of research in LegalAI. We illustrate the tasks from the perspectives of legal professionals and NLP researchers and show several representative applications in LegalAI. We conduct experiments and provide an indepth analysis of the advantages and disadvantages of existing works to explore possible future directions. You can find the implementation of our work from https://github. com/thunlp/CLAIM.

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“…The development and use of legal text processing technologies also raise a series of ethical questions, on which we focus in this paper. For example, following the publication at EMNLP 2019 of a paper on automatic prison term prediction (Chen et al, 2019) using a dataset constructed from published and publicly available records of past cases of the Supreme People's Court of China, a debate ensued about the ethical limits of legal NLP. More specifically, Leins et al (2020) queried in a systematic way whether papers such as that of Chen et al (2019) should be published.…”

Section: Introductionmentioning

confidence: 99%

On the Ethical Limits of Natural Language Processing on Legal Text

Tsarapatsanis¹,

Αλέτρας²

2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Natural language processing (NLP) methods for analyzing legal text offer legal scholars and practitioners a range of tools allowing to empirically analyze law on a large scale. However, researchers seem to struggle when it comes to identifying ethical limits to using NLP systems for acquiring genuine insights both about the law and the systems' predictive capacity. In this paper we set out a number of ways in which to think systematically about such issues. We place emphasis on three crucial normative parameters which have, to the best of our knowledge, been underestimated by current debates: (a) the importance of academic freedom, (b) the existence of a wide diversity of legal and ethical norms domestically but even more so internationally and (c) the threat of moralism in research related to computational law. For each of these three parameters we provide specific recommendations for the legal NLP community. Our discussion is structured around the study of a real-life scenario that has prompted recent debate in the legal NLP research community.

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Charge-Based Prison Term Prediction with Deep Gating Network

Cited by 60 publications

References 22 publications

Extracting Outcomes from Appellate Decisions in US State Courts

Extracting Outcomes from Appellate Decisions in US State Courts

How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence

On the Ethical Limits of Natural Language Processing on Legal Text

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