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

In relation classification, recognizing unseen (new) relations for which there are no training instances is a challenging task. We propose a prompt-based model with semantic knowledge augmentation (ZS-SKA) to recognize unseen relations under the zero-shot setting. We present a new word-level sentence translation rule and generate augmented instances with unseen relations from instances with seen relations using that new rule. We design prompts based on an external knowledge graph to integrate semantic knowledge information learned from seen relations. Instead of using the actual label sets in the prompt template, we construct weighted virtual label words. We learn the representations of both seen and unseen relations with augmented instances and prompts. We then calculate the distance between the generated representations using prototypical networks to predict unseen relations. Extensive experiments conducted on three public datasets show that ZS-SKA outperforms state-of-the-art methods under the zero-shot scenarios. Our experimental results also demonstrate the effectiveness and robustness of ZS-SKA.

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