  Rhetorical Role Labeling (RRL) of legal judgments is essential for various
tasks, such as case summarization, semantic search and argument mining.
However, it presents challenges such as inferring sentence roles from context,
interrelated roles, limited annotated data, and label imbalance. This study
introduces novel techniques to enhance RRL performance by leveraging knowledge
from semantically similar instances (neighbours). We explore inference-based
and training-based approaches, achieving remarkable improvements in challenging
macro-F1 scores. For inference-based methods, we explore interpolation
techniques that bolster label predictions without re-training. While in
training-based methods, we integrate prototypical learning with our novel
discourse-aware contrastive method that work directly on embedding spaces.
Additionally, we assess the cross-domain applicability of our methods,
demonstrating their effectiveness in transferring knowledge across diverse
legal domains.
