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

TL;DR

This paper introduces ZS-SKA, a prompt-based zero-shot relation extraction model that leverages semantic knowledge augmentation and analogy-based sentence translation to recognize unseen relations effectively.

Contribution

The paper proposes a novel semantic knowledge augmentation method using analogy-based sentence translation and weighted virtual labels for improved zero-shot relation extraction.

Findings

ZS-SKA outperforms existing methods on three datasets

The approach effectively recognizes unseen relations

The model demonstrates robustness across datasets

Abstract

In relation triplet extraction (RTE), recognizing unseen relations for which there are no training instances is a challenging task. Efforts have been made to recognize unseen relations based on question-answering models or relation descriptions. However, these approaches miss the semantic information about connections between seen and unseen relations. In this paper, 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 analogy-based sentence translation rule and generate augmented instances with unseen relations from instances with seen relations using that new rule. We design prompts with weighted virtual label construction 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 FewRel, Wiki-ZSL, and NYT, show that ZS-SKA outperforms other methods under zero-shot setting. Results also demonstrate the effectiveness and robustness of ZS-SKA.