Linguistic representations for fewer-shot relation extraction across domains

Sireesh Gururaja, Ritam Dutt, Tinglong Liao, Carolyn Rose

Abstract: Recent work has demonstrated the positive im- pact 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 rep- resentations enhance generalizability by pro- viding features that function as cross-domain pivots. We focus on the task of relation ex- traction 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 extrac- tion in multiple domains. We find that while the inclusion of these graphs results in signifi- cantly higher performance in few-shot transfer, both types of graph exhibit roughly equivalent utility.