Zero-Shot Relation Triplet Extraction via Retrieval-Augmented Synthetic Data Generation

In response to the challenge of existing relation triplet extraction models struggling to adapt to new relation categories in zero-shot scenarios, we propose a method that combines generated synthetic training data with the retrieval of relevant documents through a rank-based filtering approach for data augmentation. This approach alleviates the problem of low-quality synthetic training data and reduces noise that may affect the accuracy of triplet extraction in certain relation categories. Experimental results on two public datasets demonstrate that our model exhibits stable and impressive performance compared to the baseline models in terms of precision, recall, and F1 score, resulting in improved effectiveness for zero-shot relation triplet extraction.