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.

翻译：近期研究表明，将语言表示作为额外语境与支撑框架引入，能够对多项自然语言处理任务的域内性能产生积极影响。本研究通过探索语言表示在少样本迁移场景中对跨领域性能的影响，对该方向进行了拓展延伸。一个关键问题在于：语言表示能否通过提供可充当跨领域支点的特征来增强泛化能力？我们聚焦于关系抽取任务，在烹饪与材料科学两个领域的三组过程文本数据集上展开实验。我们的方法通过交替整合由免费现成工具构建的句法图与语义图，对主流Transformer架构进行了增强。我们考察了这些图谱在提升泛化性能方面的效用，并探究了先前发现（例如语义表示比句法表示更具优势）是否适用于多领域的关系抽取任务。实验结果表明，尽管引入这些图谱能显著提升少样本迁移性能，但两类图谱的效用大致相当。