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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.<\/jats:p>","DOI":"10.1007\/s11280-023-01216-5","type":"journal-article","created":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T00:01:48Z","timestamp":1698192108000},"page":"3983-3999","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["KC-GEE: knowledge-based conditioning for generative event extraction"],"prefix":"10.1007","volume":"26","author":[{"given":"Tongtong","family":"Wu","sequence":"first","affiliation":[]},{"given":"Fatemeh","family":"Shiri","sequence":"additional","affiliation":[]},{"given":"Jingqi","family":"Kang","sequence":"additional","affiliation":[]},{"given":"Guilin","family":"Qi","sequence":"additional","affiliation":[]},{"given":"Gholamreza","family":"Haffari","sequence":"additional","affiliation":[]},{"given":"Yuan-Fang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,25]]},"reference":[{"key":"1216_CR1","doi-asserted-by":"crossref","unstructured":"Li, Q., Peng, H., Li, J., Hei, Y., Sun, R., Sheng, J., Guo, S., Wang, L., Yu, P.S.: A survey on deep learning event extraction: Approaches and applications. 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