{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T01:20:30Z","timestamp":1742952030469,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":19,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819970216"},{"type":"electronic","value":"9789819970223"}],"license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-7022-3_14","type":"book-chapter","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:02:57Z","timestamp":1699574577000},"page":"145-158","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ancient Chinese Machine Reading Comprehension Exception Question Dataset with\u00a0a\u00a0Non-trivial Model"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6306-6811","authenticated-orcid":false,"given":"Dongning","family":"Rao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanju","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4216-106X","authenticated-orcid":false,"given":"Zhihua","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"14_CR1","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1162\/tacl_a_00338","volume":"8","author":"M Bartolo","year":"2020","unstructured":"Bartolo, M., Roberts, A., Welbl, J., Riedel, S., Stenetorp, P.: Beat the AI: investigating adversarial human annotation for reading comprehension. Trans. Assoc. Comput. Linguist. 8, 662\u2013678 (2020)","journal-title":"Trans. Assoc. Comput. Linguist."},{"unstructured":"Beltagy, I., Peters, M.E., Cohan, A.: Longformer: the long-document transformer. arXiv preprint arXiv:2004.05150 (2020)","key":"14_CR2"},{"doi-asserted-by":"crossref","unstructured":"Dzendzik, D., Foster, J., Vogel, C.: English machine reading comprehension datasets: a survey. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 8784\u20138804 (2021)","key":"14_CR3","DOI":"10.18653\/v1\/2021.emnlp-main.693"},{"unstructured":"Jiang, Y., et al.: Improving machine reading comprehension with single-choice decision and transfer learning. arXiv abs\/2011.03292 (2020)","key":"14_CR4"},{"doi-asserted-by":"crossref","unstructured":"Lai, G., Xie, Q., Liu, H., Yang, Y., Hovy, E.: Race: large-scale reading comprehension dataset from examinations. In: EMNLP (2017)","key":"14_CR5","DOI":"10.18653\/v1\/D17-1082"},{"unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: Albert: a lite bert for self-supervised learning of language representations. arXiv abs\/1909.11942 (2020)","key":"14_CR6"},{"unstructured":"Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)","key":"14_CR7"},{"doi-asserted-by":"crossref","unstructured":"Putri, R.A., Oh, A.H.: IDK-MRC: unanswerable questions for Indonesian machine reading comprehension. In: The 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022. EMNLP (2022)","key":"14_CR8","DOI":"10.18653\/v1\/2022.emnlp-main.465"},{"issue":"1","key":"14_CR9","first-page":"5485","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(1), 5485\u20135551 (2020)","journal-title":"J. Mach. Learn. Res."},{"doi-asserted-by":"crossref","unstructured":"Sugawara, S., Inui, K., Sekine, S., Aizawa, A.: What makes reading comprehension questions easier? In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4208\u20134219 (2018)","key":"14_CR10","DOI":"10.18653\/v1\/D18-1453"},{"key":"14_CR11","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1162\/tacl_a_00305","volume":"8","author":"K Sun","year":"2020","unstructured":"Sun, K., Yu, D., Yu, D., Cardie, C.: Investigating prior knowledge for challenging Chinese machine reading comprehension. Trans. Assoc. Comput. Linguist. 8, 141\u2013155 (2020)","journal-title":"Trans. Assoc. Comput. Linguist."},{"doi-asserted-by":"crossref","unstructured":"Tan, H., et al.: GCRC: a new challenging MRC dataset from Gaokao Chinese for explainable evaluation. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 1319\u20131330 (2021)","key":"14_CR12","DOI":"10.18653\/v1\/2021.findings-acl.113"},{"doi-asserted-by":"crossref","unstructured":"Tian, H., Yang, K., Liu, D., Lv, J.: Anchibert: a pre-trained model for ancient Chinese language understanding and generation. In: Proceedings of the International Joint Conference on Neural Networks (2021)","key":"14_CR13","DOI":"10.1109\/IJCNN52387.2021.9534342"},{"doi-asserted-by":"crossref","unstructured":"Wang, S., Huang, M., Deng, Z., et al.: Densely connected CNN with multi-scale feature attention for text classification. In: IJCAI, vol. 18, pp. 4468\u20134474 (2018)","key":"14_CR14","DOI":"10.24963\/ijcai.2018\/621"},{"unstructured":"Xu, S., Liu, Y., Yi, X., Zhou, S., Li, H., Wu, Y.: Native Chinese reader: a dataset towards native-level Chinese machine reading comprehension. In: Thirty-Fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) (2022)","key":"14_CR15"},{"unstructured":"Yu, W., Jiang, Z., Dong, Y., Feng, J.: Reclor: a reading comprehension dataset requiring logical reasoning. In: International Conference on Learning Representations (ICLR) (2020)","key":"14_CR16"},{"unstructured":"Zeng, W., et al.: Pangu-$$\\alpha $$: large-scale autoregressive pretrained Chinese language models with auto-parallel computation. arXiv preprint arXiv:2104.12369 (2021)","key":"14_CR17"},{"doi-asserted-by":"crossref","unstructured":"Zhang, C., Lai, Y., Feng, Y., Zhao, D.: Extract, integrate, compete: towards verification style reading comprehension. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 2976\u20132986 (2021)","key":"14_CR18","DOI":"10.18653\/v1\/2021.findings-emnlp.255"},{"doi-asserted-by":"crossref","unstructured":"Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable ConvNets V2: more deformable, better results. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9308\u20139316 (2019)","key":"14_CR19","DOI":"10.1109\/CVPR.2019.00953"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2023: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-7022-3_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:10:09Z","timestamp":1699575009000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-7022-3_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,10]]},"ISBN":["9789819970216","9789819970223"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-7022-3_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,10]]},"assertion":[{"value":"10 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jakarta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","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":"15 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2023\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"422","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":"95","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":"36","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":"23% - 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":"3.4","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":"3.1","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}