# Mining Legal Arguments in Court Decisions

arXiv: https://arxiv.org/abs/2208.06178 · HTML: https://mlsanity.com/paper/2208.06178/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 62 references that are in the map.
- First posted on arXiv 12 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset](https://mlsanity.com/paper/2104.08671/) (2021)
- [LEGAL-BERT: The Muppets straight out of Law School](https://mlsanity.com/paper/2010.02559/) (2020)
- [Argumentation Mining in User-Generated Web Discourse](https://mlsanity.com/paper/1601.02403/) (2016)

## Built on by (most cited first, 30 of 35)

- [LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development](https://mlsanity.com/paper/2305.07507/) (2023)
- [Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges](https://mlsanity.com/paper/2410.21306/) (2024)
- [An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification](https://mlsanity.com/paper/2210.05529/) (2022)
- [From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification](https://mlsanity.com/paper/2310.11878/) (2023)
- [Privacy-Preserving Models for Legal Natural Language Processing](https://mlsanity.com/paper/2211.02956/) (2022)
- [LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain](https://mlsanity.com/paper/2404.02127/) (2024)
- [Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking](https://mlsanity.com/paper/2306.00672/) (2023)
- [LexAbSumm: Aspect-based Summarization of Legal Decisions](https://mlsanity.com/paper/2404.00594/) (2024)
- [HDT: Hierarchical Document Transformer](https://mlsanity.com/paper/2407.08330/) (2024)
- [Limited Generalizability in Argument Mining: State-Of-The-Art Models Learn Datasets, Not Arguments](https://mlsanity.com/paper/2505.22137/) (2025)
- [Towards Explainability in Legal Outcome Prediction Models](https://mlsanity.com/paper/2403.16852/) (2024)
- [Query-driven Relevant Paragraph Extraction from Legal Judgments](https://mlsanity.com/paper/2404.00595/) (2024)
- [Data-efficient End-to-end Information Extraction for Statistical Legal Analysis](https://mlsanity.com/paper/2211.01692/) (2022)
- [ASP2LJ : An Adversarial Self-Play Laywer Augmented Legal Judgment Framework](https://mlsanity.com/paper/2506.18768/) (2025)
- [Legal-Tech Open Diaries: Lesson learned on how to develop and deploy light-weight models in the era of humongous Language Models](https://mlsanity.com/paper/2210.13086/) (2022)
- [Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?](https://mlsanity.com/paper/2306.04314/) (2023)
- [LaCour!: Enabling Research on Argumentation in Hearings of the European Court of Human Rights](https://mlsanity.com/paper/2312.05061/) (2023)
- [AI and the Law: Evaluating ChatGPT's Performance in Legal Classification](https://mlsanity.com/paper/2502.12193/) (2025)
- [Mining Legal Arguments to Study Judicial Formalism](https://mlsanity.com/paper/2512.11374/) (2025)
- [Compact Prompting in Instruction-tuned LLMs for Joint Argumentative Component Detection](https://mlsanity.com/paper/2603.03095/) (2026)
- [NESTLE: a No-Code Tool for Statistical Analysis of Legal Corpus](https://mlsanity.com/paper/2309.04146/) (2023)
- [ECtHR-PCR: A Dataset for Precedent Understanding and Prior Case Retrieval in the European Court of Human Rights](https://mlsanity.com/paper/2404.00596/) (2024)
- [Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?](https://mlsanity.com/paper/2406.10974/) (2024)
- [Arg-LLaDA: Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinement](https://mlsanity.com/paper/2507.19081/) (2025)
- [What Are the Facts? Automated Extraction of Court-Established Facts from Criminal-Court Opinions](https://mlsanity.com/paper/2511.05320/) (2025)
- [Assessing and Explaining the Persuadability of Large Language Models as Legal Decision Tools](https://mlsanity.com/paper/2604.26233/) (2026)
- [Structural Dilemmas and Developmental Pathways of Legal Argument Mining in the Era of Artificial Intelligence](https://mlsanity.com/paper/2605.02308/) (2026)
- [Traceable by Design: An LLM Pipeline and Dashboard for EU Regulatory Consultation Analysis](https://mlsanity.com/paper/2605.30995/) (2026)
- [From Judgments to Issues: Structured Extraction of Legal Reasoning with Citation-Hallucination Control](https://mlsanity.com/paper/2607.03325/) (2026)
- [Discourse-Aware Policy Analysis with Argumentation: A Hybrid LLM-Symbolic Framework for Disaster Governance](https://mlsanity.com/paper/2607.13260/) (2026)

## Abstract (arXiv)

Identifying, classifying, and analyzing arguments in legal discourse has been a prominent area of research since the inception of the argument mining field. However, there has been a major discrepancy between the way natural language processing (NLP) researchers model and annotate arguments in court decisions and the way legal experts understand and analyze legal argumentation. While computational approaches typically simplify arguments into generic premises and claims, arguments in legal research usually exhibit a rich typology that is important for gaining insights into the particular case and applications of law in general. We address this problem and make several substantial contributions to move the field forward. First, we design a new annotation scheme for legal arguments in proceedings of the European Court of Human Rights (ECHR) that is deeply rooted in the theory and practice of legal argumentation research. Second, we compile and annotate a large corpus of 373 court decisions (2.3M tokens and 15k annotated argument spans). Finally, we train an argument mining model that outperforms state-of-the-art models in the legal NLP domain and provide a thorough expert-based evaluation. All datasets and source codes are available under open lincenses at https://github.com/trusthlt/mining-legal-arguments.