Argument Mining with Graph Representation Learning

Zhang, Gechuan; Nulty, Paul; Lillis, David

doi:10.1145/3594536.3595152

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…We have used their model as one of the baselines. Other techniques for legal argument extraction are (Mochales and Moens, 2011;Trautmann, 2020;Xu and Ashley, 2023;Zhang et al, 2023;Santin et al, 2023).…”

Section: Related Workmentioning

confidence: 99%

Legal Argument Extraction from Court Judgements using Integer Linear Programming

Ali,

Pawar,

Palshikar

et al. 2023

Proceedings of the 10th Workshop on Argument Mining

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Legal arguments are one of the key aspects of legal knowledge which are expressed in various ways in the unstructured text of court judgements. A large database of past legal arguments can be created by extracting arguments from court judgements, categorizing them, and storing them in a structured format. Such a database would be useful for suggesting suitable arguments for any new case. In this paper, we focus on extracting arguments from Indian Supreme Court judgements using minimal supervision. We first identify a set of certain sentence-level argument markers which are useful for argument extraction such as whether a sentence contains a claim or not, whether a sentence is argumentative in nature, whether two sentences are part of the same argument, etc. We then model the legal argument extraction problem as a text segmentation problem where we combine multiple weak evidences in the form of argument markers using Integer Linear Programming (ILP), finally arriving at a global document-level solution giving the most optimal legal arguments. We demonstrate the effectiveness of our technique by comparing it against several competent baselines.

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Section: Related Workmentioning

confidence: 99%

Legal Argument Extraction from Court Judgements using Integer Linear Programming

Ali,

Pawar,

Palshikar

et al. 2023

Proceedings of the 10th Workshop on Argument Mining

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AI, Law and beyond. A transdisciplinary ecosystem for the future of AI & Law

Bex

2024

Artif Intell Law

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We live in exciting times for AI and Law: technical developments are moving at a breakneck pace, and at the same time, the call for more robust AI governance and regulation grows stronger. How should we as an AI & Law community navigate these dramatic developments and claims? In this Presidential Address, I present my ideas for a way forward: researching, developing and evaluating real AI systems for the legal field with researchers from AI, Law and beyond. I will demonstrate how we at the Netherlands National Police Lab AI are developing responsible AI by combining insights from different disciplines, and how this connects to the future of our field.

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Performance analysis of large language models in the domain of legal argument mining

Al Zubaer,

Granitzer,

Mitrović

2023

Front. Artif. Intell.

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Generative pre-trained transformers (GPT) have recently demonstrated excellent performance in various natural language tasks. The development of ChatGPT and the recently released GPT-4 model has shown competence in solving complex and higher-order reasoning tasks without further training or fine-tuning. However, the applicability and strength of these models in classifying legal texts in the context of argument mining are yet to be realized and have not been tested thoroughly. In this study, we investigate the effectiveness of GPT-like models, specifically GPT-3.5 and GPT-4, for argument mining via prompting. We closely study the model's performance considering diverse prompt formulation and example selection in the prompt via semantic search using state-of-the-art embedding models from OpenAI and sentence transformers. We primarily concentrate on the argument component classification task on the legal corpus from the European Court of Human Rights. To address these models' inherent non-deterministic nature and make our result statistically sound, we conducted 5-fold cross-validation on the test set. Our experiments demonstrate, quite surprisingly, that relatively small domain-specific models outperform GPT 3.5 and GPT-4 in the F1-score for premise and conclusion classes, with 1.9% and 12% improvements, respectively. We hypothesize that the performance drop indirectly reflects the complexity of the structure in the dataset, which we verify through prompt and data analysis. Nevertheless, our results demonstrate a noteworthy variation in the performance of GPT models based on prompt formulation. We observe comparable performance between the two embedding models, with a slight improvement in the local model's ability for prompt selection. This suggests that local models are as semantically rich as the embeddings from the OpenAI model. Our results indicate that the structure of prompts significantly impacts the performance of GPT models and should be considered when designing them.

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Argument Mining with Graph Representation Learning

Cited by 3 publications

References 37 publications

Legal Argument Extraction from Court Judgements using Integer Linear Programming

Legal Argument Extraction from Court Judgements using Integer Linear Programming

AI, Law and beyond. A transdisciplinary ecosystem for the future of AI & Law

Performance analysis of large language models in the domain of legal argument mining

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