{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T07:14:51Z","timestamp":1760426091932,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031171192"},{"type":"electronic","value":"9783031171208"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-17120-8_32","type":"book-chapter","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T13:02:58Z","timestamp":1663938178000},"page":"405-417","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["LoCSGN: Logic-Contrast Semantic Graph Network for\u00a0Machine Reading Comprehension"],"prefix":"10.1007","author":[{"given":"Xi","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Tingrui","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yuxiao","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Guiquan","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"key":"32_CR1","doi-asserted-by":"crossref","unstructured":"Anchi\u00eata, R., Pardo, T.: Semantically inspired AMR alignment for the Portuguese language. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1595\u20131600 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.123"},{"key":"32_CR2","doi-asserted-by":"crossref","unstructured":"Bai, X., Chen, Y., Song, L., Zhang, Y.: Semantic representation for dialogue modeling. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pp. 4430\u20134445 (2021)","DOI":"10.18653\/v1\/2021.acl-long.342"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Bai, X., Chen, Y., Zhang, Y.: Graph pre-training for AMR parsing and generation. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), p. todo, May 2022","DOI":"10.18653\/v1\/2022.acl-long.415"},{"key":"32_CR4","unstructured":"Banarescu, L., et al.: Abstract meaning representation for sembanking. In: Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pp. 178\u2013186. Association for Computational Linguistics (2013)"},{"key":"32_CR5","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Blloshmi, R., Navigli, R.: One spring to rule them both: symmetric AMR semantic parsing and generation without a complex pipeline. In: Proceedings of the AAAI Conference on Artificial Intelligence (2021)","DOI":"10.1609\/aaai.v35i14.17489"},{"key":"32_CR6","doi-asserted-by":"crossref","unstructured":"Blodgett, A., Schneider, N.: Probabilistic, structure-aware algorithms for improved variety, accuracy, and coverage of AMR alignments. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics, August 2021","DOI":"10.18653\/v1\/2021.acl-long.257"},{"key":"32_CR7","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709 (2020)"},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Huang, Y., Fang, M., Cao, Y., Wang, L., Liang, X.: DAGN: discourse-aware graph network for logical reasoning. In: NAACL (2021)","DOI":"10.18653\/v1\/2021.naacl-main.467"},{"key":"32_CR9","unstructured":"Hurley, P.J.: A concise introduction to logic. Cengage Learning (2014)"},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Jiao, F., Guo, Y., Song, X., Nie, L.: MERIt: meta-path guided contrastive learning for logical reasoning. In: Findings of ACL. ACL (2022)","DOI":"10.18653\/v1\/2022.findings-acl.276"},{"key":"32_CR11","doi-asserted-by":"crossref","unstructured":"Kapanipathi, P., et al.: Leveraging abstract meaning representation for knowledge base question answering. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 3884\u20133894 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.339"},{"key":"32_CR12","unstructured":"Lam, H.T., et al.: Ensembling graph predictions for AMR parsing. In: Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, 6\u201314 December 2021, virtual (2021)"},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"Li, X., Cheng, G., Chen, Z., Sun, Y., Qu, Y.: AdaLoGN: adaptive logic graph network for reasoning-based machine reading comprehension. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pp. 7147\u20137161 (2022)","DOI":"10.18653\/v1\/2022.acl-long.494"},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Lim, J., Oh, D., Jang, Y., Yang, K., Lim, H.S.: I know what you asked: graph path learning using AMR for commonsense reasoning. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 2459\u20132471 (2020)","DOI":"10.18653\/v1\/2020.coling-main.222"},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Liu, F., Flanigan, J., Thomson, S., Sadeh, N., Smith, N.A.: Toward abstractive summarization using semantic representations. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1077\u20131086 (2015)","DOI":"10.3115\/v1\/N15-1114"},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Liu, J., Cui, L., Liu, H., Huang, D., Wang, Y., Zhang, Y.: LogiQA: a challenge dataset for machine reading comprehension with logical reasoning. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 3622\u20133628 (2021)","DOI":"10.24963\/ijcai.2020\/501"},{"key":"32_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Y., Che, W., Zheng, B., Qin, B., Liu, T.: An AMR aligner tuned by transition-based parser. In: EMNLP (2018)","DOI":"10.18653\/v1\/D18-1264"},{"key":"32_CR18","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach (2020)"},{"key":"32_CR19","unstructured":"Ouyang, S., Zhang, Z., Zhao, H.: Fact-driven logical reasoning. arXiv preprint arXiv:2105.10334 (2021)"},{"key":"32_CR20","doi-asserted-by":"crossref","unstructured":"Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: Squad: 100,000+ questions for machine comprehension of text. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2383\u20132392 (2016)","DOI":"10.18653\/v1\/D16-1264"},{"key":"32_CR21","doi-asserted-by":"crossref","unstructured":"Schlichtkrull, M.S., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: ESWC (2018)","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"32_CR22","doi-asserted-by":"crossref","unstructured":"Song, L., Gildea, D., Zhang, Y., Wang, Z., Su, J.: Semantic neural machine translation using AMR. Trans. Assoc. Comput. Linguist. 7, 19\u201331 (2019)","DOI":"10.1162\/tacl_a_00252"},{"key":"32_CR23","doi-asserted-by":"crossref","unstructured":"Wang, S., et al.: From LSAT: the progress and challenges of complex reasoning. IEEE\/ACM Trans. Audio Speech Lang. Process. (2022)","DOI":"10.1109\/TASLP.2022.3164218"},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Wang, S., et al.: Logic-driven context extension and data augmentation for logical reasoning of text (2021)","DOI":"10.18653\/v1\/2022.findings-acl.127"},{"key":"32_CR25","doi-asserted-by":"crossref","unstructured":"Xu, W., Zhang, H., Cai, D., Lam, W.: Dynamic semantic graph construction and reasoning for explainable multi-hop science question answering. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 1044\u20131056 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.90"},{"key":"32_CR26","doi-asserted-by":"crossref","unstructured":"Yang, Z., et al.: HotpotQA: a dataset for diverse, explainable multi-hop question answering. In: Conference on Empirical Methods in Natural Language Processing (EMNLP) (2018)","DOI":"10.18653\/v1\/D18-1259"},{"key":"32_CR27","unstructured":"Yu, W., Jiang, Z., Dong, Y., Feng, J.: ReClor: a reading comprehension dataset requiring logical reasoning. In: International Conference on Learning Representations (ICLR), April 2020"},{"key":"32_CR28","doi-asserted-by":"crossref","unstructured":"Zhou, J., Naseem, T., Astudillo, R.F., Florian, R.: AMR parsing with action-pointer transformer. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 5585\u20135598 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.443"}],"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\/978-3-031-17120-8_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T13:08:01Z","timestamp":1663938481000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17120-8_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031171192","9783031171208"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17120-8_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 September 2022","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":"Guilin","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2022\/","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":"327","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":"73","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":"22% - 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":"1.5","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)"}}]}}