# Toward an Intelligent Tutoring System for Argument Mining in Legal Texts

arXiv: https://arxiv.org/abs/2210.13635 · HTML: https://mlsanity.com/paper/2210.13635/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Identification of Rhetorical Roles of Sentences in Indian Legal Judgments](https://mlsanity.com/paper/1911.05405/) (2019)
- [Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review](https://mlsanity.com/paper/1504.06868/) (2015)
- [Computer-Assisted Creation of Boolean Search Rules for Text Classification in the Legal Domain](https://mlsanity.com/paper/2112.05807/) (2021)
- [Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains](https://mlsanity.com/paper/2112.07882/) (2021)
- [Sentence Embeddings and High-speed Similarity Search for Fast Computer Assisted Annotation of Legal Documents](https://mlsanity.com/paper/2112.11494/) (2021)
- [Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data](https://mlsanity.com/paper/2112.07870/) (2021)
- [Data-Centric Machine Learning in the Legal Domain](https://mlsanity.com/paper/2201.06653/) (2022)

## Built on by (most cited first, 1 of 1)

- [From Text to Structure: Using Large Language Models to Support the Development of Legal Expert Systems](https://mlsanity.com/paper/2311.04911/) (2023)

## Abstract (arXiv)

We propose an adaptive environment (CABINET) to support caselaw analysis (identifying key argument elements) based on a novel cognitive computing framework that carefully matches various machine learning (ML) capabilities to the proficiency of a user. CABINET supports law students in their learning as well as professionals in their work. The results of our experiments focused on the feasibility of the proposed framework are promising. We show that the system is capable of identifying a potential error in the analysis with very low false positives rate (2.0-3.5%), as well as of predicting the key argument element type (e.g., an issue or a holding) with a reasonably high F1-score (0.74).