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.

翻译：在本文中,我们用多种颗粒度来探讨法律论据的采矿。辩论采矿通常被概念化为一个句子分类问题。在这项工作中,我们把辩论采矿概念化为一个象征性(即字级)分类问题。我们用长式模型来分类象征物。结果显示,象征性文本分类比判决级文本分类更准确地确定了某些法律论据要素。 Token级别分类也为分析法律文本和更深入地了解模型在处理大量输入数据时所侧重的内容提供了更大的灵活性。