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.

通过在关系抽取任务中引入语言表示和语义图，我们扩展了现有研究，并探讨了它们对跨领域性能的影响。我们发现，这些语言表示和语义图可以显著提高少样本学习的性能，并且在不同领域中具有相当的实用性。