Three Heads Better than One: Pure Entity, Relation Label and Adversarial Training for Cross-domain Few-shot Relation Extraction

Abstract

ABSTRACT In this paper, we study cross-domain relation extraction. Since new data mapping to feature spaces always differs from the previously seen data due to a domain shift, few-shot relation extraction often perform poorly. To solve the problems caused by cross-domain, we propose a method for combining the pure entity, relation labels and adversarial (PERLA). We first use entities and complete sentences for separate encoding to obtain context-independent entity features. Then, we combine relation labels which are useful for relation extraction to mitigate context noise. We combine adversarial to reduce the noise caused by cross-domain. We conducted experiments on the publicly available cross-domain relation extraction dataset Fewrel 2.0[1]①, and the results show that our approach improves accuracy and has better transferability for better adaptation to cross-domain tasks.