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Automatic Judgment Prediction via Legal Reading Comprehension

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Shangbang Long, Cunchao Tu, Zhiyuan Liu, Maosong Sun

Artificial Intelligence · Submitted 18 Sep 2018

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experimental results on this dataset demonstrate that our model achieves significant improvement over state-of-the-art models.

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arXiv ID1809.06537 (version 1)
Pith ID3W4LKGVR, Pith's permanent record of this paper and its review
CitationCunchao Tu, Maosong Sun, Shangbang Long, Zhiyuan Liu. "Automatic Judgment Prediction via Legal Reading Comprehension." arXiv:1809.06537 (2018). https://pith.science/paper/3W4LKGVR
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What the abstract itself states, as probabilities: claims first 0.08 · states limitation 0.07 · human participants 0.08 · controlled comparison 0.64 · randomized assignment 0.01 · states quantitative result 0.06 · compares with prior results 0.95 · mentions code or data availability 0.96.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Automatic judgment prediction aims to predict the judicial results based on case materials. It has been studied for several decades mainly by lawyers and judges, considered as a novel and prospective application of artificial intelligence techniques in the legal field. Most existing methods follow the text classification framework, which fails to model the complex interactions among complementary case materials. To address this issue, we formalize the task as Legal Reading Comprehension according to the legal scenario. Following the working protocol of human judges, LRC predicts the final judgment results based on three types of information, including fact description, plaintiffs' pleas, and law articles. Moreover, we propose a novel LRC model, AutoJudge, which captures the complex semantic interactions among facts, pleas, and laws. In experiments, we construct a real-world civil case dataset for LRC. Experimental results on this dataset demonstrate that our model achieves significant improvement over state-of-the-art models. We will publish all source codes and datasets of this work on \urlgithub.com for further research.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Automatic judgment prediction aims to predict the judicial results based on case materials. background 1.00 0.06 0.03 asserted 0.99
s1 It has been studied for several decades mainly by lawyers and judges, considered as a novel and prospective application of artificial intelligence techniques in the legal field. background 1.00 0.04 0.02 asserted 1.00
s2 Most existing methods follow the text classification framework, which fails to model the complex interactions among complementary case materials. gap 0.94 0.07 0.03 asserted 1.00
s3 To address this issue, we formalize the task as Legal Reading Comprehension according to the legal scenario. method 0.51 0.87 0.05 asserted 0.98
s4 Following the working protocol of human judges, LRC predicts the final judgment results based on three types of information, including fact description, plaintiffs' pleas, and law articles. method 0.97 0.54 0.06 asserted 1.00
s5 Moreover, we propose a novel LRC model, AutoJudge, which captures the complex semantic interactions among facts, pleas, and laws. contribution 0.75 0.94 0.17 asserted 1.00
s6 In experiments, we construct a real-world civil case dataset for LRC. method 0.83 0.95 0.03 asserted 1.00
s7 Experimental results on this dataset demonstrate that our model achieves significant improvement over state-of-the-art models. result 1.00 0.96 0.75 asserted 1.00
s8 We will publish all source codes and datasets of this work on \urlgithub.com for further research. future work 0.90 0.51 0.02 asserted 0.95

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 1.00.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Automatic judgment prediction aims to predict the judicial results based on case materials. It has been studied for several decades mainly by lawyers and judges, considered as a novel and prospective application of artificial intelligence techniques in the legal field. Most existing methods follow the text classification framework, which fails to model the complex interactions among complementary case materials. To address this issue, we formalize the task as Legal Reading Comprehension according to the legal scenario. Following the working protocol of human judges, LRC predicts the final judgment results based on three types of information, including fact description, plaintiffs' pleas, and law articles. Moreover, we propose a novel LRC model, AutoJudge, which captures the complex semantic interactions among facts, pleas, and laws. In experiments, we construct a real-world civil case dataset for LRC. Experimental results on this dataset demonstrate that our model achieves significant improvement over state-of-the-art models. We will publish all source codes and datasets of this work on \urlgithub.com for further research.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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Cites as background 2 papers
  • Conventional reading comprehension [ref] usually considers reading comprehension as predicting the answer given a passage and a query, where the answer could be a single word, a text span of the original passage, chosen from answer candidates, or generated by human annotators.

    MS MARCO: A Human Generated MAchine Reading COmprehension Dataset · Conventional Reading Comprehension · 0.98

  • Conventional reading comprehension [ref] usually considers reading comprehension as predicting the answer given a passage and a query, where the answer could be a single word, a text span of the original passage, chosen from answer candidates, or generated by human annotators.

    DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications · Conventional Reading Comprehension · 0.98

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