{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T22:44:28Z","timestamp":1753051468222,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031446924"},{"type":"electronic","value":"9783031446931"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44693-1_57","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T08:02:39Z","timestamp":1696665759000},"page":"736-748","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["rT5: A Retrieval-Augmented Pre-trained Model for\u00a0Ancient Chinese Entity Description Generation"],"prefix":"10.1007","author":[{"given":"Mengting","family":"Hu","sequence":"first","affiliation":[]},{"given":"Xiaoqun","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Jiaqi","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Jianfeng","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Xiaosu","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Zhengdan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yike","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Yufei","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Yuzhi","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"issue":"1","key":"57_CR1","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/TKDE.2020.2981314","volume":"34","author":"J Li","year":"2020","unstructured":"Li, J., Sun, A., Han, J., Li, C.: A survey on deep learning for named entity recognition. IEEE Trans. Knowl. Data Eng. 34(1), 50\u201370 (2020)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"57_CR2","doi-asserted-by":"crossref","unstructured":"Li, J.: Generating classical Chinese poems via conditional variational autoencoder and adversarial training. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3890\u20133900. Association for Computational Linguistics, Brussels, Belgium, October\u2013November 2018","DOI":"10.18653\/v1\/D18-1423"},{"key":"57_CR3","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, J., Zhang, B., Jin, Q.: Research and implementation of Chinese couplet generation system with attention based transformer mechanism. IEEE Trans. Comput. Soc. Syst. (2021)","DOI":"10.1109\/TCSS.2021.3072153"},{"key":"57_CR4","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"57_CR5","unstructured":"Izacard, G., et al.: Unsupervised dense information retrieval with contrastive learning (2021)"},{"key":"57_CR6","unstructured":"Raffel, C.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(1), 5485\u20135551 (2020)"},{"key":"57_CR7","doi-asserted-by":"crossref","unstructured":"Xue, L.: mT5: a massively multilingual pre-trained text-to-text transformer. arXiv preprint arXiv:2010.11934 (2020)","DOI":"10.18653\/v1\/2021.naacl-main.41"},{"key":"57_CR8","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311\u2013318. Association for Computational Linguistics, Philadelphia, Pennsylvania, USA, July 2002","DOI":"10.3115\/1073083.1073135"},{"key":"57_CR9","unstructured":"Lin, C.-Y.: ROUGE: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74\u201381. Association for Computational Linguistics, Barcelona, Spain, July 2004"},{"key":"57_CR10","unstructured":"Banerjee, S., Lavie, A.: METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and\/or Summarization, pp. 65\u201372. Association for Computational Linguistics, Ann Arbor, Michigan, June 2005"},{"key":"57_CR11","doi-asserted-by":"crossref","unstructured":"Lewis, M.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"57_CR12","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al.: Improving language understanding by generative pre-training (2018)"},{"key":"57_CR13","unstructured":"Dong, L.: Unified language model pre-training for natural language understanding and generation. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"57_CR14","unstructured":"Liu, Y.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"57_CR15","doi-asserted-by":"crossref","unstructured":"Chang, Y., Kong, L., Jia, K., Meng, Q.: Chinese named entity recognition method based on BERT. In: 2021 IEEE International Conference on Data Science and Computer Application (ICDSCA), pp. 294\u2013299 (2021)","DOI":"10.1109\/ICDSCA53499.2021.9650256"},{"key":"57_CR16","doi-asserted-by":"crossref","unstructured":"Yang, Z., et al.: Generating classical Chinese poems from vernacular Chinese. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. In: Conference on Empirical Methods in Natural Language Processing, vol. 2019, p. 6155. NIH Public Access (2019)","DOI":"10.18653\/v1\/D19-1637"},{"key":"57_CR17","doi-asserted-by":"crossref","unstructured":"Yuan, S., Zhong, L., Li, L., Zhang, R.: Automatic generation of Chinese couplets with attention based encoder-decoder model. In: 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 65\u201370. IEEE (2019)","DOI":"10.1109\/MIPR.2019.00020"},{"key":"57_CR18","unstructured":"Guu, K., Lee, K., Tung, Z., Pasupat, P., Chang, M.: Retrieval augmented language model pre-training. In: International Conference on Machine Learning, pp. 3929\u20133938. PMLR (2020)"},{"key":"57_CR19","doi-asserted-by":"crossref","unstructured":"Wang, H.: Retrieval enhanced model for commonsense generation. arXiv preprint arXiv:2105.11174 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.269"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44693-1_57","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T08:28:30Z","timestamp":1696840110000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44693-1_57"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031446924","9783031446931"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44693-1_57","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Foshan","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2023\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Softconf","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"478","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"143","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}