# An interpretability framework for Similar case matching

arXiv: https://arxiv.org/abs/2304.01622 · HTML: https://mlsanity.com/paper/2304.01622/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 12 of 12)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [Adversarial Training Methods for Semi-Supervised Text Classification](https://mlsanity.com/paper/1605.07725/) (2016)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1705.00609/) (2017)
- [How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://mlsanity.com/paper/2004.12158/) (2020)
- [Interpretable Adversarial Perturbation in Input Embedding Space for Text](https://mlsanity.com/paper/1805.02917/) (2018)
- [CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain](https://mlsanity.com/paper/1911.08962/) (2019)
- [Building Legal Case Retrieval Systems with Lexical Matching and Summarization using A Pre-Trained Phrase Scoring Model](https://mlsanity.com/paper/2009.14083/) (2020)
- [Legal Document Retrieval using Document Vector Embeddings and Deep Learning](https://mlsanity.com/paper/1805.10685/) (2018)
- [An Uncommon Task: Participatory Design in Legal AI](https://mlsanity.com/paper/2203.06246/) (2022)

## Abstract (arXiv)

Similar Case Matching (SCM) plays a pivotal role in the legal system by facilitating the efficient identification of similar cases for legal professionals. While previous research has primarily concentrated on enhancing the performance of SCM models, the aspect of interpretability has been neglected. To bridge the gap, this study proposes an integrated pipeline framework for interpretable SCM. The framework comprises four modules: judicial feature sentence identification, case matching, feature sentence alignment, and conflict resolution. In contrast to current SCM methods, our framework first extracts feature sentences within a legal case that contain essential information. Then it conducts case matching based on these extracted features. Subsequently, our framework aligns the corresponding sentences in two legal cases to provide evidence of similarity. In instances where the results of case matching and feature sentence alignment exhibit conflicts, the conflict resolution module resolves these inconsistencies. The experimental results show the effectiveness of our proposed framework, establishing a new benchmark for interpretable SCM.