{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T10:59:33Z","timestamp":1780916373039,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819500130","type":"print"},{"value":"9789819500147","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-95-0014-7_22","type":"book-chapter","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T10:05:51Z","timestamp":1753351551000},"page":"257-268","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Instruction Tuning with Data Augmentation for Event Argument Extraction"],"prefix":"10.1007","author":[{"given":"Chengfei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanhuan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,25]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Nguyen, M.V., Lai, V.D., Nguyen, T.H.: Cross-task instance representation interactions and label dependencies for joint information extraction with graph convolutional networks. In: NAACL, pp. 27\u201338 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.3"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Liu, W., Cheng, S., Zeng, D., et al.: Enhancing document-level event argument extraction with contextual clues and role relevance. In: ACL, pp. 12908\u201312922 (2023)","DOI":"10.18653\/v1\/2023.findings-acl.817"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, X., Han, X., et al.: CLEVE: Contrastive pre-training for event extraction. In: ACL, pp. 6283\u20136297 (2021)","DOI":"10.18653\/v1\/2021.acl-long.491"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Wang, S., Yu, M., Chang, S., et al.: Query and extract: Refining event extraction as type-oriented binary decoding. In: ACL, pp. 169\u2013182 (2022)","DOI":"10.18653\/v1\/2022.findings-acl.16"},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Li, F., Peng, W., Chen, Y., et al.: Event extraction as multi-turn question answering. In: EMNLP, pp. 829\u2013838 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.73"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Lu, D., Ran, S., Tetreault, J., et al.: Event extraction as question generation and answering. In: ACL, pp. 1666\u20131688 (2023)","DOI":"10.18653\/v1\/2023.acl-short.143"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Hsu, I.H., Huang, K.H., Boschee, E., et al.: DEGREE: A data-efficient generation-based event extraction model. In: NAACL, pp. 1890\u20131908 (2022)","DOI":"10.18653\/v1\/2022.naacl-main.138"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Ma, M.D., Taylor, A., Wang, W., et al.: DICE: Data-efficient clinical event extraction with generative models. In: ACL, pp. 15898\u201315917 (2023)","DOI":"10.18653\/v1\/2023.acl-long.886"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Lewis, M., Liu, Y., Goyal, N., et al.: BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: ACL, pp. 7871\u20137880 (2020)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"issue":"140","key":"22_CR10","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., Shazeer, N., Roberts, A., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR 21(140), 1\u201367 (2020)","journal-title":"JMLR"},{"issue":"70","key":"22_CR11","first-page":"1","volume":"25","author":"HW Chung","year":"2024","unstructured":"Chung, H.W., Hou, L., Longpre, S., et al.: Scaling instruction-finetuned language models. JMLR 25(70), 1\u201353 (2024)","journal-title":"JMLR"},{"key":"22_CR12","unstructured":"Touvron, H., Lavril, T., Izacard, G., et al.: LLAMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)"},{"key":"22_CR13","unstructured":"Gao, J., Zhao, H., Yu, C., et al.: Exploring the feasibility of chatgpt for event extraction. arXiv preprint arXiv:2303.03836 (2023)"},{"key":"22_CR14","unstructured":"Wei, X., Cui, X., Cheng, N., et al.: Zero-shot information extraction via chatting with chatgpt. arXiv preprint arXiv:2302.10205 (2023)"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Ho, N., Schmid, L., Yun, S.Y.: Large language models are reasoning teachers. In: ACL, pp. 14852\u201314882 (2023)","DOI":"10.18653\/v1\/2023.acl-long.830"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Wadhwa, S., Amir, S., Wallace, B.: Revisiting relation extraction in the era of large language models. In: ACL, pp. 15566\u201315589 (2023)","DOI":"10.18653\/v1\/2023.acl-long.868"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Li, S., Ji, H., Han, J.: Document-level event argument extraction by conditional generation. In: NAACL, pp. 894\u2013908 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.69"},{"key":"22_CR18","doi-asserted-by":"crossref","unstructured":"Ebner, S., Xia, P., Culkin, R., et al.: Multi-sentence argument linking. In: ACL, pp. 8057\u20138077 (2020)","DOI":"10.18653\/v1\/2020.acl-main.718"},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Ma, Y., Wang, Z., Cao, Y., et al.: Prompt for extraction? PAIE: Prompting argument interaction for event argument extraction. In: ACL, pp. 6759\u20136774 (2022)","DOI":"10.18653\/v1\/2022.acl-long.466"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Du, X., Cardie, C.: Event extraction by answering (almost) natural questions. In: EMNLP, pp. 671\u2013683 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.49"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Ren, Y., Cao, Y., Guo, P., et al.: Retrieve-and-sample: Document-level event argument extraction via hybrid retrieval augmentation. In: ACL, pp. 293\u2013306 (2023)","DOI":"10.18653\/v1\/2023.acl-long.17"},{"key":"22_CR22","unstructured":"Ding, G., Guo, X., Wang, X., et al.: Fusion meets function: The adaptive selection-generation approach in event argument extraction. In: COLING, pp. 4359\u20134369(2025)"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"He, S., Du, W., Peng, X., et al.: Multi-hierarchical error-aware contrastive learning for event argument extraction. Knowledge-Based Systems, p. 112889 (2025)","DOI":"10.1016\/j.knosys.2024.112889"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Xu, J., Song, D., Hui, S., et al.: Separation and fusion: A novel multiple token linking model for event argument extraction. In: NAACL, pp. 6611\u20136624 (2024)","DOI":"10.18653\/v1\/2024.naacl-long.368"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0014-7_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T10:04:57Z","timestamp":1780913097000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0014-7_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819500130","9789819500147"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0014-7_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}