--- title: "Legal Judgment Prediction via Topological — Zhong 2018" description: "The paper addresses Legal Judgment Prediction (LJP) in civil law systems." authors: ["Haoxi Zhong", "Zhipeng Guo", "Cunchao Tu", "Chaojun Xiao", "Zhiyuan Liu", "Maosong Sun"] year: 2018 venue: "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing" source: "https://aclanthology.org/D18-1390/" doi: "10.18653/v1/d18-1390" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/zhong-2018-legal-judgment-prediction-topological-learning/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhong-2018-legal-judgment-prediction-topological-learning/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2018](https://md.research.elamur.ai/free/en/papers/2018/page/3/) / Legal Judgment Prediction via Topological Learning # Legal Judgment Prediction via Topological Learning **Authors:** [Haoxi Zhong](https://md.research.elamur.ai/free/en/authors/haoxi-zhong/), [Zhipeng Guo](https://md.research.elamur.ai/free/en/authors/zhipeng-guo/), [Cunchao Tu](https://md.research.elamur.ai/free/en/authors/cunchao-tu/), [Chaojun Xiao](https://md.research.elamur.ai/free/en/authors/chaojun-xiao/), [Zhiyuan Liu](https://md.research.elamur.ai/free/en/authors/zhiyuan-liu/), [Maosong Sun](https://md.research.elamur.ai/free/en/authors/maosong-sun/) · **Year:** 2018 · **Venue:** Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/D18-1390/) · [PDF](https://aclanthology.org/D18-1390.pdf) · [DOI 10.18653/v1/d18-1390](https://doi.org/10.18653/v1/d18-1390) · [OpenAlex](https://openalex.org/W2890026792) **Citations:** 344 citations (openalex-snapshot · 2026-10-14) · [136](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary The paper addresses Legal Judgment Prediction (LJP) in civil law systems. Earlier work mostly treated it as a single text classification problem, such as predicting only the charge. In practice, a judgment has several linked parts. A judge first identifies the applicable law articles, then decides the charges they imply, and finally sets the term of penalty and fines. The authors model these ordered dependencies with a Directed Acyclic Graph (DAG), and on this basis they introduce TopJudge, a multi-task framework. It encodes the fact description once and then solves the subtasks in topological order. Each subtask's prediction is conditioned on the outputs of the subtasks it depends on, and the design can accommodate any DAG of subtasks. Experiments on three large real-world collections of Chinese criminal cases covered law articles, charges and term of penalty. TopJudge consistently beat single-task baselines and conventional multi-task models. The work matters because enforcing legal ordering reduces contradictory outputs, for example a charge that does not fit the predicted sentence. It also makes the predictions easier to interpret. ## Key points - Subtask dependencies are formalized as a DAG. Each subtask is predicted by an RNN cell whose initialization carries information from its prerequisite subtasks, combined with task-specific representations. Ablations show that both components are needed for the gains. - On the PKU dataset, TopJudge reaches about 95% accuracy for law articles and charges, with macro-F1 of 68.4 and 70.9. For term of penalty it reaches 57.8 accuracy and 31.8 macro-F1. Removing all dependencies lowers term-of-penalty macro-F1 to 27.6. - In case studies, CNN-MTL produced inconsistent outputs, such as an arson charge paired with a 1–2 year term, or confusion between similar charges. TopJudge avoided these by using its law-article predictions. - Remaining errors come from class imbalance, since rare outcomes like life imprisonment have few training cases. They also come from fact descriptions that omit sentencing-relevant details, such as whether the defendant is a minor. - The code is released at github.com/thunlp/TopJudge. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) · [Law](https://md.research.elamur.ai/free/en/shelves/law/) ## Cited by Cited by 136 papers of our library cite it (of the 140884 whose bibliographies we read); 11 of them have a page here, 125 not yet. - [ALJP: An Arabic Legal Judgment Prediction in Personal Status Cases Using Machine Learning Models](https://md.research.elamur.ai/free/en/papers/abbara-2023-aljp-arabic-legal-judgment-prediction/) - [Neural Legal Judgment Prediction in English](https://md.research.elamur.ai/free/en/papers/chalkidis-2019-neural-legal-judgment-prediction-english/) - [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-lexglue-benchmark-dataset-legal-language/) - [Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-paragraph-level-rationale-extraction-through/) - [LawBench: Benchmarking Legal Knowledge of Large Language Models](https://md.research.elamur.ai/free/en/papers/fei-2023-lawbench-benchmarking-legal-knowledge-large/) - [LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/guha-2023-legalbench-collaboratively-built-benchmark-measuring/) - [Lawyer LLaMA Technical Report](https://md.research.elamur.ai/free/en/papers/huang-2023-lawyer-llama-technical-report/) - [ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation](https://md.research.elamur.ai/free/en/papers/malik-2021-ildc-cjpe-indian-legal-documents/) - [Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark](https://md.research.elamur.ai/free/en/papers/niklaus-2021-swiss-judgment-prediction-multilingual-legal/) - [Distinguish Confusing Law Articles for Legal Judgment Prediction](https://md.research.elamur.ai/free/en/papers/xu-2020-distinguish-confusing-law-articles-legal/) - [How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://md.research.elamur.ai/free/en/papers/zhong-2020-how-does-nlp-benefit-legal/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{zhong2018legal, title = {{Legal Judgment Prediction via Topological Learning}}, author = {Zhong, Haoxi and Guo, Zhipeng and Tu, Cunchao and Xiao, Chaojun and Liu, Zhiyuan and Sun, Maosong}, booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}, year = {2018}, doi = {10.18653/v1/d18-1390}, url = {https://doi.org/10.18653/v1/d18-1390} } ``` - [← previous Learning Word Vectors for 157 Languages](https://md.research.elamur.ai/free/en/papers/grave-2018-learning-word-vectors-157-languages/) - [next → Lessons from Natural Language Inference in the Clinical Domain](https://md.research.elamur.ai/free/en/papers/romanov-2018-lessons-natural-language-inference-clinical/) - [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/3/) ## References 40 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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