Multi-granularity Argument Mining in Legal Texts
This work addresses argument mining in legal texts, offering incremental improvements in granularity and interpretability for legal professionals and researchers.
The paper tackles legal argument mining by shifting from sentence-level to token-level classification using a Longformer model, resulting in more accurate identification of legal argument elements and greater flexibility for analysis.
In this paper, we explore legal argument mining using multiple levels of granularity. Argument mining has usually been conceptualized as a sentence classification problem. In this work, we conceptualize argument mining as a token-level (i.e., word-level) classification problem. We use a Longformer model to classify the tokens. Results show that token-level text classification identifies certain legal argument elements more accurately than sentence-level text classification. Token-level classification also provides greater flexibility to analyze legal texts and to gain more insight into what the model focuses on when processing a large amount of input data.