{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T19:33:28Z","timestamp":1784748808364,"version":"3.55.0"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030454418","type":"print"},{"value":"9783030454425","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-45442-5_45","type":"book-chapter","created":{"date-parts":[[2020,4,10]],"date-time":"2020-04-10T21:03:47Z","timestamp":1586552627000},"page":"359-366","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["BERT for Evidence Retrieval and Claim Verification"],"prefix":"10.1007","author":[{"given":"Amir","family":"Soleimani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christof","family":"Monz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcel","family":"Worring","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,4,8]]},"reference":[{"key":"45_CR1","unstructured":"Alberti, C., Lee, K., Collins, M.: A BERT baseline for the natural questions. arXiv preprint arXiv:1901.08634 (2019)"},{"key":"45_CR2","unstructured":"Bajaj, P., et al.: MS MARCO: a human generated machine reading comprehension dataset. arXiv preprint arXiv:1611.09268 (2016)"},{"key":"45_CR3","doi-asserted-by":"crossref","unstructured":"Burges, C., et al.: Learning to rank using gradient descent. In: Proceedings of the 22nd International Conference on Machine learning, ICML 2005, pp. 89\u201396 (2005)","DOI":"10.1145\/1102351.1102363"},{"key":"45_CR4","doi-asserted-by":"crossref","unstructured":"Chen, D., Fisch, A., Weston, J., Bordes, A.: Reading Wikipedia to answer open-domain questions. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1870\u20131879 (2017)","DOI":"10.18653\/v1\/P17-1171"},{"key":"45_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Q., Zhu, X., Ling, Z., Wei, S., Jiang, H.: Enhancing and combining sequential and tree LSTM for natural language inference. arXiv preprint arXiv:1609.06038 (2016)","DOI":"10.18653\/v1\/P17-1152"},{"key":"45_CR6","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":"45_CR7","doi-asserted-by":"publisher","unstructured":"Hanselowski, A., et al.: UKP-Athene: multi-sentence textual entailment for claim verification. In: Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pp. 103\u2013108. Association for Computational Linguistics, Brussels, November 2018. https:\/\/doi.org\/10.18653\/v1\/W18-5516","DOI":"10.18653\/v1\/W18-5516"},{"key":"45_CR8","doi-asserted-by":"publisher","unstructured":"Malon, C.: Team Papelo: transformer networks at FEVER. In: Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pp. 109\u2013113. Association for Computational Linguistics, Brussels, November 2018. https:\/\/doi.org\/10.18653\/v1\/W18-5517","DOI":"10.18653\/v1\/W18-5517"},{"key":"45_CR9","doi-asserted-by":"publisher","unstructured":"Nie, Y., Chen, H., Bansal, M.: Combining fact extraction and verification with neural semantic matching networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 6859\u20136866 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33016859","DOI":"10.1609\/aaai.v33i01.33016859"},{"key":"45_CR10","doi-asserted-by":"crossref","unstructured":"Parikh, A.P., T\u00e4ckstr\u00f6m, O., Das, D., Uszkoreit, J.: A decomposable attention model for natural language inference. arXiv preprint arXiv:1606.01933 (2016)","DOI":"10.18653\/v1\/D16-1244"},{"key":"45_CR11","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training. Technical report, OpenAI (2018)"},{"key":"45_CR12","doi-asserted-by":"crossref","unstructured":"Sakata, W., Shibata, T., Tanaka, R., Kurohashi, S.: FAQ retrieval using query-question similarity and BERT-based query-answer relevance. In: SIGIR 2019: 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1113\u20131116 (2019)","DOI":"10.1145\/3331184.3331326"},{"key":"45_CR13","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"45_CR14","doi-asserted-by":"crossref","unstructured":"Thorne, J., Vlachos, A., Christodoulopoulos, C., Mittal, A.: FEVER: a large-scale dataset for fact extraction and verification. arXiv preprint arXiv:1803.05355 (2018)","DOI":"10.18653\/v1\/N18-1074"},{"key":"45_CR15","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding. arXiv preprint arXiv:1906.08237 (2019)"},{"key":"45_CR16","doi-asserted-by":"publisher","unstructured":"Yoneda, T., Mitchell, J., Welbl, J., Stenetorp, P., Riedel, S.: UCL machine reading group: four factor framework for fact finding (HexaF). In: Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pp. 97\u2013102. Association for Computational Linguistics, Brussels, November 2018. https:\/\/doi.org\/10.18653\/v1\/W18-5515","DOI":"10.18653\/v1\/W18-5515"},{"key":"45_CR17","doi-asserted-by":"crossref","unstructured":"Zhong, W., et al.: Reasoning over semantic-level graph for fact checking. arXiv preprint arXiv:1909.03745 (2019)","DOI":"10.18653\/v1\/2020.acl-main.549"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-45442-5_45","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:29:07Z","timestamp":1710358147000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-45442-5_45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030454418","9783030454425"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-45442-5_45","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"8 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"42","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2020.org\/","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":"457","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":"55","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":"46","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":"12% - 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":"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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Also included: 8 reproducibility papers, 10 demonstration papers, 12 CLEF organizers lab track papers, 7 doctoral consortium papers, 4 workshops, 3 tutorials. Due to the COVID-19 pandemic, this conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}