Segmentation and Rhetorical Role Labeling of legal judgements play a crucial
role in retrieval and adjacent tasks, including case summarization, semantic
search, argument mining etc. Previous approaches have formulated this task
either as independent classification or sequence labeling of sentences. In this
work, we reformulate the task at span level as identifying spans of multiple
consecutive sentences that share the same rhetorical role label to be assigned
via classification. We employ semi-Markov Conditional Random Fields (CRF) to
jointly learn span segmentation and span label assignment. We further explore
three data augmentation strategies to mitigate the data scarcity in the
specialized domain of law where individual documents tend to be very long and
annotation cost is high. Our experiments demonstrate improvement of span-level
prediction metrics with a semi-Markov CRF model over a CRF baseline. This
benefit is contingent on the presence of multi sentence spans in the document.

本文研究了在法律文件中识别多句子共享同一修辞角色标签的跨度分割问题，使用了半马尔可夫条件随机场模型进行标签分类，辅助三种数据增强策略，通过实验证明了该方法在提升跨度级预测度量方面的优点，需要文件中具有多句子跨度。