{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:07:22Z","timestamp":1742980042897,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":16,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819916412"},{"type":"electronic","value":"9789819916429"}],"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\/978-981-99-1642-9_32","type":"book-chapter","created":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T12:14:57Z","timestamp":1681388097000},"page":"373-384","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Extractive Question Answering Using Transformer-Based LM"],"prefix":"10.1007","author":[{"given":"Raj","family":"Jha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V. Susheela","family":"Devi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"32_CR1","unstructured":"Boyer, J.M.: Natural language question answering in the financial domain. In: Onut, I.V., Jaramillo, A., Jourdan, G.-V., Petriu, D.C., Chen, W., (eds.) Proceedings of the 28th Annual International Conference on Computer Science and Software Engineering, CASCON 2018, Markham, Ontario, Canada, 29\u201331 October 2018, pp. 189\u2013200. ACM (2018)"},{"key":"32_CR2","unstructured":"Araci, D.: FinBERT: financial sentiment analysis with pre-trained language models. CoRR, abs\/1908.10063 (2019)"},{"key":"32_CR3","unstructured":"Devlin, J., Chang, M. W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2\u20137 June 2019, vol. 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics (2019)"},{"key":"32_CR4","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al., (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4\u20139 December 2017, Long Beach, CA, USA, pp. 5998\u20136008 (2017)"},{"key":"32_CR5","unstructured":"Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.R.: GLUE: a multi-task benchmark and analysis platform for natural language understanding. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6\u20139 May 2019. OpenReview.net (2019)"},{"key":"32_CR6","unstructured":"McCann, B., Keskar, N.S., Xiong, C., Socher, R.: The natural language decathlon: multitask learning as question answering. CoRR, abs\/1806.08730 (2018)"},{"key":"32_CR7","doi-asserted-by":"crossref","unstructured":"Bjerva, J., Bhutani, N., Golshan, B., Tan, W.C., Augenstein, I.: SubjQA: a dataset for subjectivity and review comprehension. In: Webber, B., Cohn, T., He, Y., Liu, Y., (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, 16\u201320 November 2020, pp. 5480\u20135494. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.442"},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Feng, G., et al.: Question classification by approximating semantics. In: Gangemi, A., Leonardi, S., Panconesi, A., (eds.) Proceedings of the 24th International Conference on World Wide Web Companion, WWW 2015, Florence, Italy, 18\u201322 May 2015 - Companion Volume, pp. 407\u2013417. ACM (2015)","DOI":"10.1145\/2740908.2745403"},{"key":"32_CR9","unstructured":"Yih, S.W.T., Chang, M.-W., Meek, C., Pastusiak, A.: Question answering using enhanced lexical semantic models. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013, 4\u20139 August 2013, Sofia, Bulgaria, vol. 1: Long Papers, pp. 1744\u20131753. The Association for Computer Linguistics (2013)"},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Tran, N.K., et al.: A neural network-based framework for non-factoid question answering. In: Champin, P.A., Gandon, F., Lalmas, M., Ipeirotis, P.G., (eds.) Companion of the the Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon, France, 23\u201327 April 2018, pp. 1979\u20131983. ACM (2018)","DOI":"10.1145\/3184558.3191830"},{"key":"32_CR11","doi-asserted-by":"crossref","unstructured":"Feng, M., Xiang, B., Glass, M. R., Wang, L., Zhou, B.: Applying deep learning to answer selection: a study and an open task. In: 2015 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2015, Scottsdale, AZ, USA, 13\u201317 December 2015, pp. 813\u2013820. IEEE (2015)","DOI":"10.1109\/ASRU.2015.7404872"},{"key":"32_CR12","unstructured":"Tan, M., Xiang, B., Zhou, B.: LSTM-based deep learning models for non-factoid answer selection. CoRR, abs\/1511.04108 (2015)"},{"key":"32_CR13","unstructured":"Wang, S., Jiang, J.: A compare-aggregate model for matching text sequences. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Conference Track Proceedings. Open-Review.net (2017)"},{"key":"32_CR14","unstructured":"Liu, Y.: RoBERTa: a robustly optimized BERT pretraining approach. CoRR, abs\/1907.11692 (2019)"},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Maia, M., et al.: Www\u201918 open challenge: financial opinion mining and question answering. In: Champin, P.-A., Gandon, F., Lalmas, M., Ipeirotis, P.G., (eds.) Companion of the the Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon, France, 23\u201327 April 2018, pp. 1941\u20131942. ACM (2018)","DOI":"10.1145\/3184558.3192301"},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Mogotsi, I.C., Manning, C.D., Raghavan, P., Sch\u00fctze, H.: Introduction to information retrieval. Inf. Retr. 13(2), 192\u2013195 (2010). Cambridge University Press, Cambridge, England, 2008, pp. 482, ISBN 978-0-521-86571-5","DOI":"10.1007\/s10791-009-9115-y"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-1642-9_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T12:30:34Z","timestamp":1681389034000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-1642-9_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819916412","9789819916429"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-1642-9_32","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"14 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"22 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2022.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"810","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":"359","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":"44% - 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":"2.65","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","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":"ICONIP 2022 consists of a two-volume set, LNCS & CCIS, which includes 146 and 213 papers","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)"}}]}}