1. KC-GEE: knowledge-based conditioning for generative event extraction.
- Author
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Wu, Tongtong, Shiri, Fatemeh, Kang, Jingqi, Qi, Guilin, Haffari, Gholamreza, and Li, Yuan-Fang
- Subjects
LANGUAGE models ,SUPERVISED learning - Abstract
Event extraction is an important, but challenging task. Many existing techniques decompose it into event and argument detection/classification subtasks, which are complex structured prediction problems. Generation-based extraction techniques lessen the complexity of the problem formulation and are able to leverage the reasoning capabilities of large pretrained language models. However, they still suffer from poor zero-shot generalizability and are ineffective in handling long contexts such as documents. We propose a generative event extraction model, KC-GEE, that addresses these limitations. A key contribution of KC-GEE is a novel knowledge-based conditioning technique that injects the schema of candidate event types as the prefix into each layer of an encoder-decoder language model. This enables effective zero-shot learning and improves supervised learning. Our experiments on two benchmark datasets demonstrate the strong performance of our KC-GEE model. It achieves particularly strong results in the challenging document-level extraction task and in the zero-shot learning setting, outperforming state-of-the-art models by up to 5.4 absolute F1 points. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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