  Recent work has demonstrated the positive impact of incorporating linguistic
representations as additional context and scaffolding on the in-domain
performance of several NLP tasks. We extend this work by exploring the impact
of linguistic representations on cross-domain performance in a few-shot
transfer setting. An important question is whether linguistic representations
enhance generalizability by providing features that function as cross-domain
pivots. We focus on the task of relation extraction on three datasets of
procedural text in two domains, cooking and materials science. Our approach
augments a popular transformer-based architecture by alternately incorporating
syntactic and semantic graphs constructed by freely available off-the-shelf
tools. We examine their utility for enhancing generalization, and investigate
whether earlier findings, e.g. that semantic representations can be more
helpful than syntactic ones, extend to relation extraction in multiple domains.
We find that while the inclusion of these graphs results in significantly
higher performance in few-shot transfer, both types of graph exhibit roughly
equivalent utility.
