Distinguish Confusing Law Articles for Legal Judgment Prediction

Xu, Nuo; Wang, Pinghui; Chen, Long; Pan, Li; Wang, Xiaoyan; Zhao, Junzhou

doi:10.18653/v1/2020.acl-main.280

Cited by 88 publications

(50 citation statements)

References 11 publications

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“…Then we compare GCI and two integration methods with NN baselines, including LSTM, Bi-LSTM and Bi-LSTM+Att. Bi-LSTM+Att is a common backbone of legal judgement prediction models, while we do not add multitask learning (Luo et al, 2017) and expert knowledge (Xu et al, 2020) for simplicity. Since the prior knowledge learned from pre-trained models may result in unfair comparison, we do not choose the models such as BERT (Devlin et al, 2018) as baselines and backbones to eliminate the influence.…”

Section: Methodsmentioning

confidence: 99%

“…Zhong et al (2020) provide interpretable judgements by iteratively questioning and answering. Another line pays attention to confusing charges: manually design discriminative attributes, and Xu et al (2020) use attention mechanisms to highlight differences between similar charges. Using knowledge derived from causal graphs, GCI exhibits a different and interpretable discrimination process.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis

Liu¹,

Yin²,

Feng³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Causal inference is the process of capturing cause-effect relationship among variables. Most existing works focus on dealing with structured data, while mining causal relationship among factors from unstructured data, like text, has been less examined, but is of great importance, especially in the legal domain. In this paper, we propose a novel Graph-based Causal Inference (GCI) framework, which builds causal graphs from fact descriptions without much human involvement and enables causal inference to facilitate legal practitioners to make proper decisions. We evaluate the framework on a challenging similar charge disambiguation task. Experimental results show that GCI can capture the nuance from fact descriptions among multiple confusing charges and provide explainable discrimination, especially in few-shot settings. We also observe that the causal knowledge contained in GCI can be effectively injected into powerful neural networks for better performance and interpretability. Code and data are available at https://github.com/xxxiaol/GCI/.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis

Liu¹,

Yin²,

Feng³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

View full text Add to dashboard Cite

show abstract

“…[7] represents the faction of using discriminative legal attributes for judgment prediction which emphasizes more on the judicial fairness during prediction. [26] stands for the research works based on distinguishing law articles for judgment prediction which has been proved to be effective especially for criminal cases. [2] leverages BERT to focus only on learning good representation of the pure input fact text for judgment prediction.…”

Section: Related Work 21 Legal Judgment Predictionmentioning

confidence: 99%

Legal Judgment Prediction with Multi-Stage Case Representation Learning in the Real Court Setting

Zhang

Wang

et al. 2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Legal judgment prediction(LJP) is an essential task for legal AI. While prior methods studied on this topic in a pseudo setting by employing the judge-summarized case narrative as the input to predict the judgment, neglecting critical case life-cycle information in real court setting could threaten the case logic representation quality and prediction correctness. In this paper, we introduce a novel challenging dataset 1 from real courtrooms to predict the legal judgment in a reasonably encyclopedic manner by leveraging the genuine input of the case -plaintiff's claims and court debate data, from which the case's facts are automatically recognized by comprehensively understanding the multi-role dialogues of the court debate, and then learnt to discriminate the claims so as to reach the final judgment through multi-task learning. An extensive set of experiments with a large civil trial data set shows that the proposed model can more accurately characterize the interactions among claims, fact and debate for legal judgment prediction, achieving significant improvements over strong state-of-the-art baselines. Moreover, the user study conducted with real judges and law school students shows the neural predictions can also be interpretable and easily observed, and thus enhancing the trial efficiency and judgment quality. CCS CONCEPTS• Computing methodologies → Discourse, dialogue and pragmatics; Multi-task learning; • Applied computing → Law.

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“…For instance, note that an acyclic dependency exists between the sub-tasks, while introduce a multi-perspective forward prediction and backward verification framework to utilize result dependencies between the sub-tasks. Xu et al (2020) use graph-based methods to group statutory articles into communities, and use distinguishable features from each community to attentively encode facts. Since these methods are geared toward utilizing the correlations between the related sub-tasks (and need training data pertaining to all these tasks), we do not consider them as baselines, since our main focus is only charge identification.…”

Section: Related Workmentioning

confidence: 99%

Automatic Charge Identification from Facts: A Few Sentence-Level Charge Annotations is All You Need

Paul

Goyal

Ghosh

2020

Proceedings of the 28th International Conference on Computational Linguistics

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Automatic Charge Identification (ACI) is the task of identifying the relevant legal charges given the facts of a situation and the statutory laws that define these charges, and is a crucial aspect of the judicial process. Prior works focus on learning charge-side representations by modeling relationships between the charges, but not much effort has been made in improving fact-side representations. We observe that only a small fraction of sentences in the facts actually indicates the charges. We show that by using a very small subset (< 3%) of fact descriptions annotated with sentence-level charges, we can achieve an improvement across a range of different ACI models, as compared to modeling just the main document-level task on a much larger dataset. Additionally, we propose a novel model that utilizes sentence-level charge labels as an auxiliary task, coupled with the main task of document-level charge identification in a multi-task learning framework. The proposed model comprehensively outperforms a large number of recent baseline models for ACI. The improvement in performance is particularly noticeable for the rare charges which are known to be especially challenging to identify.

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Distinguish Confusing Law Articles for Legal Judgment Prediction

Cited by 88 publications

References 11 publications

Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis

Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis

Legal Judgment Prediction with Multi-Stage Case Representation Learning in the Real Court Setting

Automatic Charge Identification from Facts: A Few Sentence-Level Charge Annotations is All You Need

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