KC-GEE: knowledge-based conditioning for generative event extraction
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Published:2023-10-25
Issue:6
Volume:26
Page:3983-3999
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ISSN:1386-145X
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Container-title:World Wide Web
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language:en
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Short-container-title:World Wide Web
Author:
Wu Tongtong,Shiri Fatemeh,Kang Jingqi,Qi Guilin,Haffari Gholamreza,Li Yuan-Fang
Abstract
AbstractEvent 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.
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Software
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