Linguistic representations for fewer-shot relation extraction across domains
This work addresses the challenge of generalizing relation extraction models across domains with limited data, though it is incremental as it extends prior findings on linguistic representations to a new setting.
The paper tackled the problem of cross-domain few-shot relation extraction by incorporating linguistic representations as additional context, finding that both syntactic and semantic graphs significantly improved performance in few-shot transfer across cooking and materials science domains, with roughly equivalent utility between the two types.
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.