# Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction

arXiv: https://arxiv.org/abs/2207.04182 · HTML: https://mlsanity.com/paper/2207.04182/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2022.

## Built on by (most cited first, 21 of 21)

- [LLM Post-Training: A Deep Dive into Reasoning Large Language Models](https://mlsanity.com/paper/2502.21321/) (2025)
- [SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval](https://mlsanity.com/paper/2304.11370/) (2023)
- [THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case Retrieval](https://mlsanity.com/paper/2305.06812/) (2023)
- [CitaLaw: Enhancing LLM with Citations in Legal Domain](https://mlsanity.com/paper/2412.14556/) (2024)
- [Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation](https://mlsanity.com/paper/2406.19760/) (2024)
- [Bringing order into the realm of Transformer-based language models for artificial intelligence and law](https://mlsanity.com/paper/2308.05502/) (2023)
- [THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment](https://mlsanity.com/paper/2305.06817/) (2023)
- [Large Language Models Meet Legal Artificial Intelligence: A Survey](https://mlsanity.com/paper/2509.09969/) (2025)
- [MUSER: A Multi-View Similar Case Retrieval Dataset](https://mlsanity.com/paper/2310.15602/) (2023)
- [Legal Mathematical Reasoning with LLMs: Procedural Alignment through Two-Stage Reinforcement Learning](https://mlsanity.com/paper/2504.02590/) (2025)
- [An Intent Taxonomy of Legal Case Retrieval](https://mlsanity.com/paper/2307.13298/) (2023)
- [Computing Approximate Graph Edit Distance via Optimal Transport](https://mlsanity.com/paper/2412.18857/) (2024)
- [Logic Rules as Explanations for Legal Case Retrieval](https://mlsanity.com/paper/2403.01457/) (2024)
- [Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2601.15267/) (2026)
- [FAIR: A Causal Framework for Accurately Inferring Judgments Reversals](https://mlsanity.com/paper/2306.11585/) (2023)
- [Enhancing Criminal Case Matching through Diverse Legal Factors](https://mlsanity.com/paper/2406.11172/) (2024)
- [How Vital is the Jurisprudential Relevance: Law Article Intervened Legal Case Retrieval and Matching](https://mlsanity.com/paper/2502.18292/) (2025)
- [CREST: Improving Interpretability and Effectiveness of Troubleshooting at Ericsson through Criterion-Specific Trouble Report Retrieval](https://mlsanity.com/paper/2511.17417/) (2025)
- [OBJECTION! Lawyer Agents Mitigate Guilty Bias in Legal Judgment Prediction](https://mlsanity.com/paper/2609.02158/) (2026)
- [Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data](https://mlsanity.com/paper/2609.13940/) (2026)
- [Chinese-Jev: Bringing System One Model to Chinese-Language Tasks](https://mlsanity.com/paper/2609.36965/) (2026)

## Abstract (arXiv)

As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems. This task has a high demand on the explainability of matching results because of its critical impacts on downstream applications -- the matched legal cases may provide supportive evidence for the judgments of target cases and thus influence the fairness and justice of legal decisions. Focusing on this challenging task, we propose a novel and explainable method, namely textitIOT-Match, with the help of computational optimal transport, which formulates the legal case matching problem as an inverse optimal transport (IOT) problem. Different from most existing methods, which merely focus on the sentence-level semantic similarity between legal cases, our IOT-Match learns to extract rationales from paired legal cases based on both semantics and legal characteristics of their sentences. The extracted rationales are further applied to generate faithful explanations and conduct matching. Moreover, the proposed IOT-Match is robust to the alignment label insufficiency issue commonly in practical legal case matching tasks, which is suitable for both supervised and semi-supervised learning paradigms. To demonstrate the superiority of our IOT-Match method and construct a benchmark of explainable legal case matching task, we not only extend the well-known Challenge of AI in Law (CAIL) dataset but also build a new Explainable Legal cAse Matching (ELAM) dataset, which contains lots of legal cases with detailed and explainable annotations. Experiments on these two datasets show that our IOT-Match outperforms state-of-the-art methods consistently on matching prediction, rationale extraction, and explanation generation.