  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.
