{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T06:12:27Z","timestamp":1740809547471,"version":"3.38.0"},"reference-count":22,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2025,3,1]]},"DOI":"10.1587\/transinf.2024edp7137","type":"journal-article","created":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T22:14:33Z","timestamp":1727648073000},"page":"277-285","source":"Crossref","is-referenced-by-count":0,"title":["Semantic Matching Template-Based Zero-Shot Relation Triplet Extraction"],"prefix":"10.1587","volume":"E108.D","author":[{"given":"Mei","family":"ZHANG","sequence":"first","affiliation":[{"name":"College of Informatics, North China University of Technology"}]},{"given":"Yu","family":"TIAN","sequence":"additional","affiliation":[{"name":"College of Informatics, North China University of Technology"}]},{"given":"Yuechen","family":"YANG","sequence":"additional","affiliation":[{"name":"College of Informatics, North China University of Technology"}]},{"given":"Jianyong","family":"DUAN","sequence":"additional","affiliation":[{"name":"College of Informatics, North China University of Technology"}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P.J. Liu, \u201cExploring the limits of transfer learning with a unified text-to-text transformer,\u201d arXiv preprint, arXiv:1910.10683, Oct. 2019. 10.48550\/arXiv.1910.10683"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] X. Li, F. Yin, Z. Sun, X. Li, A. Yuan, D. Chai, M. Zhou, and J. Li, \u201cEntity-relation extraction as multi-turn question answering,\u201d Proc. 57th Annual Meeting of the Association for Computational Linguistics, pp.1340-1350, July 2019. 10.18653\/v1\/p19-1129","DOI":"10.18653\/v1\/P19-1129"},{"key":"3","unstructured":"[3] L. Dong, N. Yang, W. Wang, F. Wei, X. Liu, Y. Wang, J. Gao, M. Zhou, and H.W. Hon, \u201cUnified language model pre-training for natural language understanding and generation,\u201d Neural Information Processing Systems, pp.13042-13054, May 2019."},{"key":"4","unstructured":"[4] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, \u201cLanguage models are unsupervised multitask learners,\u201d OpenAI blog, vol.1, no.8, 9, 2019."},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] Y. Lu, Q. Liu, D. Dai, X. Xiao, H. Lin, X. Han, L. Sun, and H. Wu, \u201cUnified structure generation for universal information extraction,\u201d Proc. 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.5755-5772, 2022. 10.18653\/v1\/2022.acl-long.395","DOI":"10.18653\/v1\/2022.acl-long.395"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] L. Cui, Y. Wu, J. Liu, S. Yang, and Y. Zhang, \u201cTemplate-based named entity recognition using BART,\u201d Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp.1835-1845, Aug. 2021. 10.18653\/v1\/2021.findings-acl.161","DOI":"10.18653\/v1\/2021.findings-acl.161"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] D. Vrande\u010di\u0107 and M. Kr\u00f6tzsch, \u201cWikidata: A free collaborative knowledgebase,\u201d Commun. ACM, vol.57, no.10, pp.78-85, Sept. 2014. 10.1145\/2629489","DOI":"10.1145\/2629489"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] N. Reimers and I. Gurevych, \u201cSentence-BERT: Sentence embeddings using Siamese BERT-networks,\u201d arXiv preprint arXiv:1908.10084, Aug. 2019. 10.48550\/arXiv.1908.10084","DOI":"10.18653\/v1\/D19-1410"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] X. Han, H. Zhu, P. Yu, Z. Wang, Y. Yao, Z. Liu, and M. Sun, \u201cFewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation,\u201d Proc. 2018 Conference on Empirical Methods in Natural Language Processing, pp.4803-4809, Oct,-Nov. 2018. 10.18653\/v1\/d18-1514","DOI":"10.18653\/v1\/D18-1514"},{"key":"10","unstructured":"[10] Y. Meng, J. Huang, Y. Zhang, and J. Han, \u201cGenerating training data with language models: Towards zero-shot language understanding,\u201d Advances in Neural Information Processing Systems, vol.35, pp.462-477, 2022."},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] Y.K. Chia, L. Bing, S. Poria, and L. Si, \u201cRelationPrompt: Leveraging prompts to generate synthetic data for zero-shot relation triplet extraction,\u201d Findings of the Association for Computational Linguistics: ACL 2022, pp.45-57, 2022. 10.18653\/v1\/2022.findings-acl.5","DOI":"10.18653\/v1\/2022.findings-acl.5"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] T. Nayak, N. Majumder, P. Goyal, and S. Poria, \u201cDeep neural approaches to relation triplets extraction: A comprehensive survey,\u201d Cognitive Computation, vol.13, no.5, pp.1215-1232, 2021. 10.1007\/s12559-021-09917-7","DOI":"10.1007\/s12559-021-09917-7"},{"key":"13","unstructured":"[13] B. Yu, Z. Zhang, X. Shu, Y. Wang, T. Liu, B. Wang, and S. Li, \u201cJoint extraction of entities and relations based on a novel decomposition strategy,\u201d arXiv preprint, arXiv:1909.04273, Sept. 2019. 10.48550\/arXiv.1909.04273"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer, \u201cBART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,\u201d Proc. 58th Annual Meeting of the Association for Computational Linguistics, pp.7871-7880, July 2020. 10.18653\/v1\/2020.acl-main.703","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] C.-Y. Chen and C.-T. Li, \u201cZS-BERT: Towards zero-shot relation extraction with attribute representation learning,\u201d Proc. 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.3470-3479, June 2021. 10.18653\/v1\/2021.naacl-main.272","DOI":"10.18653\/v1\/2021.naacl-main.272"},{"key":"16","unstructured":"[16] Y. Ganin and V. Lempitsky, \u201cUnsupervised domain adaptation by backpropagation,\u201d Proc. 32nd International Conference on Machine Learning, pp.1180-1189, July 2015."},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] D. Sachan, M. Lewis, M. Joshi, A. Aghajanyan, W.-t. Yih, J. Pineau, and L. Zettlemoyer, \u201cImproving passage retrieval with zero-shot question generation,\u201d Proc. 2022 Conference on Empirical Methods in Natural Language Processing, pp.3781-3797, Dec. 2022. 10.18653\/v1\/2022.emnlp-main.249","DOI":"10.18653\/v1\/2022.emnlp-main.249"},{"key":"18","unstructured":"[18] N.S. Keskar, B. McCann, L.R. Varshney, C. Xiong, and R. Socher, \u201cCTRL: A conditional transformer language model for controllable generation,\u201d arXiv preprint, arXiv:1909.05858, Sept. 2019. 10.48550\/arXiv.1909.05858"},{"key":"19","unstructured":"[19] Y. Abbasi-Yadkori, D. P\u00e1l, and C. Szepesv\u00e1ri, \u201cImproved algorithms for linear stochastic bandits,\u201d Advances in Neural Information Processing Systems, vol.24, 2011."},{"key":"20","unstructured":"[20] V. Sanh, A. Webson, C. Raffel, S.H. Bach, L. Sutawika, Z. Alyafeai, A. Chaffin, A. Stiegler, T.L. Scao, A. Raja, M. Dey, M.S. Bari, C. Xu, U. Thakker, S.S. Sharma, E. Szczechla, T. Kim, G. Chhablani, N. Nayak, D. Datta, J. Chang, M.T.-J. Jiang, H. Wang, M. Manica, S. Shen, Z.X. Yong, H. Pandey, R. Bawden, T. Wang, T. Neeraj, J. Rozen, A. Sharma, A. Santilli, T. Fevry, J.A. Fries, R. Teehan, T. Bers, S. Biderman, L. Gao, T. Wolf, and A.M. Rush, \u201cMultitask prompted training enables zero-shot task generalization,\u201d arXiv preprint, arXiv:2110.08207, Oct. 2021. 10.48550\/arXiv.2110.08207"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] T. Schick, H. Schmid, and H. Sch\u00fctze, \u201cAutomatically identifying words that can serve as labels for few-shot text classification,\u201d Proc. 28th International Conference on Computational Linguistics, pp.5569-5578, Dec. 2020. 10.18653\/v1\/2020.coling-main.488","DOI":"10.18653\/v1\/2020.coling-main.488"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] J. Zhao, W. Zhan, X. Zhao, Q. Zhang, T. Gui, Z. Wei, J. Wang, M. Peng, and M. Sun, \u201cRe-Matching: A fine-grained semantic matching method for zero-shot relation extraction,\u201d Proc. 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.6680-6691, July 2023. 10.18653\/v1\/2023.acl-long.369","DOI":"10.18653\/v1\/2023.acl-long.369"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/3\/E108.D_2024EDP7137\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T03:32:54Z","timestamp":1740799974000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/3\/E108.D_2024EDP7137\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,1]]},"references-count":22,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2024edp7137","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2025,3,1]]},"article-number":"2024EDP7137"}}