Abstract
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.
This work is supported by the National Natural Science Foundation of China (61972003), R &D Program of Beijing Municipal Education Commission (KM202210009002), the Beijing Urban Governance Research Base of North China University of Technology (2023CSZL16), and the North China University of Technology Startup Fund.
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Zhang, Q. et al. (2024). Zero-Shot Relation Triplet Extraction via Retrieval-Augmented Synthetic Data Generation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1969. Springer, Singapore. https://doi.org/10.1007/978-981-99-8184-7_28
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DOI: https://doi.org/10.1007/978-981-99-8184-7_28
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