Enhancing Semantic Correlation between Instances and Relations for Zero-Shot Relation Extraction

Zero-shot relation extraction aims to recognize (new) unseen relations that cannot be observed during training. Due to this point, recognizing unseen relations with no corresponding labeled training instances is a challenging task. Recognizing an unseen relation between two entities in an input instance at the testing time, a model needs to grasp the semantic relationship between the instance and all unseen relations to make a prediction. This study argues that enhancing the semantic correlation between instances and relations is key to effectively solving the zero-shot relation extraction task. A new model entirely devoted to this goal through three main aspects was proposed: learning effective relation representation, designing purposeful mini-batches, and binding two-way semantic consistency. Experimental results on two benchmark datasets demonstrate that our approach significantly improves task performance and achieves state-of-the-art results. Our source code and data are publicly available.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Zero-shot Relation Classification FewRel ESC-ZSRE Avg. F1 81.68 # 1
Zero-shot Relation Classification Wiki-ZSL ESC-ZSRE Avg. F1 86.74 # 1


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