Zero-shot Triplet Extraction by Template Infilling

• URL: http://arxiv.org/abs/2212.10708v2
• Date: Wed, 20 Sep 2023 05:12:27 GMT
• Title: Zero-shot Triplet Extraction by Template Infilling
• Authors: Bosung Kim, Hayate Iso, Nikita Bhutani, Estevam Hruschka, Ndapa Nakashole, Tom Mitchell
• Abstract summary: Triplet extraction aims to extract pairs of entities and their corresponding relations from unstructured text. We show that by reducing triplet extraction to a template infilling task over a pre-trained language model (LM), we can equip the extraction model with zero-shot learning capabilities. We propose a novel framework, ZETT, that aligns the task objective to the pre-training objective of generative transformers to generalize to unseen relations.
• Score: 13.295751492744081
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The task of triplet extraction aims to extract pairs of entities and their corresponding relations from unstructured text. Most existing methods train an extraction model on training data involving specific target relations, and are incapable of extracting new relations that were not observed at training time. Generalizing the model to unseen relations typically requires fine-tuning on synthetic training data which is often noisy and unreliable. We show that by reducing triplet extraction to a template infilling task over a pre-trained language model (LM), we can equip the extraction model with zero-shot learning capabilities and eliminate the need for additional training data. We propose a novel framework, ZETT (ZEro-shot Triplet extraction by Template infilling), that aligns the task objective to the pre-training objective of generative transformers to generalize to unseen relations. Experiments on FewRel and Wiki-ZSL datasets demonstrate that ZETT shows consistent and stable performance, outperforming previous state-of-the-art methods, even when using automatically generated templates. https://github.com/megagonlabs/zett/

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