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In relation triplet extraction (RTE), recognizing unseen (new) relations forwhich there are no training instances is a challenging task. Efforts have beenmade to recognize unseen relations based on question-answering models orrelation descriptions. However, these approaches miss the semantic informationabout connections between seen and unseen relations. In this paper, We proposea prompt-based model with semantic knowledge augmentation (ZS-SKA) to recognizeunseen relations under the zero-shot setting. We present a new word-levelanalogy-based sentence translation rule and generate augmented instances withunseen relations from instances with seen relations using that new rule. Wedesign prompts with weighted virtual label construction based on an externalknowledge graph to integrate semantic knowledge information learned from seenrelations. Instead of using the actual label sets in the prompt template, weconstruct weighted virtual label words. We learn the representations of bothseen and unseen relations with augmented instances and prompts. We thencalculate the distance between the generated representations using prototypicalnetworks to predict unseen relations. Extensive experiments conducted on threepublic datasets FewRel, Wiki-ZSL, and NYT, show that ZS-SKA outperformsstate-of-the-art methods under the zero-shot scenarios. Our experimentalresults also demonstrate the effectiveness and robustness of ZS-SKA.

Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation