# Nonet at SemEval-2023 Task 6: Methodologies for Legal Evaluation

arXiv: https://arxiv.org/abs/2310.11049 · HTML: https://mlsanity.com/paper/2310.11049/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation](https://mlsanity.com/paper/2105.13562/) (2021)
- [Identification of Rhetorical Roles of Sentences in Indian Legal Judgments](https://mlsanity.com/paper/1911.05405/) (2019)
- [Named Entity Recognition in Indian court judgments](https://mlsanity.com/paper/2211.03442/) (2022)
- [Explainable Information Retrieval: A Survey](https://mlsanity.com/paper/2211.02405/) (2022)
- [HLDC: Hindi Legal Documents Corpus](https://mlsanity.com/paper/2204.00806/) (2022)
- [SemEval 2023 Task 6: LegalEval - Understanding Legal Texts](https://mlsanity.com/paper/2304.09548/) (2023)
- [Named Entity Recognition in the Legal Domain using a Pointer Generator Network](https://mlsanity.com/paper/2012.09936/) (2020)

## Built on by (most cited first, 9 of 9)

- [Applicability of Large Language Models and Generative Models for Legal Case Judgement Summarization](https://mlsanity.com/paper/2407.12848/) (2024)
- [Rethinking Legal Judgement Prediction in a Realistic Scenario in the Era of Large Language Models](https://mlsanity.com/paper/2410.10542/) (2024)
- [LegalSeg: Unlocking the Structure of Indian Legal Judgments Through Rhetorical Role Classification](https://mlsanity.com/paper/2502.05836/) (2025)
- [Questioning Biases in Case Judgment Summaries: Legal Datasets or Large Language Models?](https://mlsanity.com/paper/2312.00554/) (2023)
- [Code Generation and Algorithmic Problem Solving Using Llama 3.1 405B](https://mlsanity.com/paper/2409.19027/) (2024)
- [MIRROR: A Novel Approach for the Automated Evaluation of Open-Ended Question Generation](https://mlsanity.com/paper/2410.12893/) (2024)
- [Prompt Engineering Using GPT for Word-Level Code-Mixed Language Identification in Low-Resource Dravidian Languages](https://mlsanity.com/paper/2411.04025/) (2024)
- [CryptoLLM: Unleashing the Power of Prompted LLMs for SmartQnA and Classification of Crypto Posts](https://mlsanity.com/paper/2411.07917/) (2024)
- [AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System](https://mlsanity.com/paper/2607.18825/) (2026)

## Abstract (arXiv)

This paper describes our submission to the SemEval-2023 for Task 6 on LegalEval: Understanding Legal Texts. Our submission concentrated on three subtasks: Legal Named Entity Recognition (L-NER) for Task-B, Legal Judgment Prediction (LJP) for Task-C1, and Court Judgment Prediction with Explanation (CJPE) for Task-C2. We conducted various experiments on these subtasks and presented the results in detail, including data statistics and methodology. It is worth noting that legal tasks, such as those tackled in this research, have been gaining importance due to the increasing need to automate legal analysis and support. Our team obtained competitive rankings of 15th, 11th, and 1st in Task-B, Task-C1, and Task-C2, respectively, as reported on the leaderboard.