  In this work, we employ two AMR-enhanced semantic representations for ICL on
RE: one that explores the AMR structure generated for a sentence at the
subgraph level (shortest AMR path), and another that explores the full AMR
structure generated for a sentence. In both cases, we demonstrate that all
settings benefit from the fine-grained AMR's semantic structure. We evaluate
our model on four RE datasets. Our results show that our model can outperform
the GPT-based baselines, and achieve SOTA performance on two of the datasets,
and competitive performance on the other two.
