{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:19:09Z","timestamp":1775229549891,"version":"3.50.1"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031333798","type":"print"},{"value":"9783031333804","type":"electronic"}],"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\/978-3-031-33380-4_5","type":"book-chapter","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T08:05:58Z","timestamp":1685088358000},"page":"58-69","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Let the\u00a0Model Make Financial Senses: A Text2Text Generative Approach for\u00a0Financial Complaint Identification"],"prefix":"10.1007","author":[{"given":"Sarmistha","family":"Das","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2020-4751","authenticated-orcid":false,"given":"Apoorva","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raghav","family":"Jain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5458-9381","authenticated-orcid":false,"given":"Sriparna","family":"Saha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alka","family":"Maurya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,27]]},"reference":[{"key":"5_CR1","unstructured":"Trosborg, A.: Interlanguage Pragmatics: Requests, Complaints, and Apologies, vol. 7. Walter de Gruyter, Berlin (2011)"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Preotiuc-Pietro, D., Gaman, M., Aletras N.: Automatically identifying complaints in social media. arXiv preprint arXiv:1906.03890 (2019)","DOI":"10.18653\/v1\/P19-1495"},{"issue":"5","key":"5_CR3","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1007\/s12559-021-09925-7","volume":"13","author":"S Poria","year":"2021","unstructured":"Poria, S., et al.: Recognizing emotion cause in conversations. Cogn. Comput. 13(5), 1317\u20131332 (2021)","journal-title":"Cogn. Comput."},{"key":"5_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1007\/978-3-030-86331-9_46","volume-title":"Document Analysis and Recognition","author":"A Singh","year":"2021","unstructured":"Singh, A., Saha, S.: Are you really complaining? A multi-task framework for complaint identification, emotion, and sentiment classification. In: Llad\u00f3s, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12822, pp. 715\u2013731. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86331-9_46"},{"key":"5_CR5","unstructured":"Wu, S.: Automating Knowledge Distillation and Representation from Richly Formatted Data. Stanford University, Stanford (2020)"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Bhatia, R., Singh, A., Saha, S.: Complaint and severity identification from online financial content (2022)","DOI":"10.36227\/techrxiv.20087675"},{"key":"5_CR7","unstructured":"Toutanova, K., et al.: In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)"},{"key":"5_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1007\/978-3-030-99736-6_29","volume-title":"Advances in Information Retrieval","author":"A Singh","year":"2022","unstructured":"Singh, A., Nazir, A., Saha, S.: Adversarial multi-task model for emotion, sentiment, and sarcasm aided complaint detection. In: Hagen, M., et al. (eds.) ECIR 2022. LNCS, vol. 13185, pp. 428\u2013442. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-99736-6_29"},{"key":"5_CR9","unstructured":"Lee, S.Y.M., Chen, Y., Huang, C.-R.: A text-driven rule-based system for emotion cause detection. In: Proceedings of the NAACL HLT 2010 Workshop On Computational Approaches to Analysis and Generation of Emotion in Text, pp. 45\u201353 (2010)"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Sabour, S., Zheng, C., Huang, M.: CEM: commonsense-aware empathetic response generation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 10, pp. 11 229\u201311 237 (2022)","DOI":"10.1609\/aaai.v36i10.21373"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Speer, R., Chin, J., Havasi, C.: ConceptNet 5.5: an open multilingual graph of general knowledge. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.11164"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Sap, M., et al.: ATOMIC: an atlas of machine commonsense for if-then reasoning. In: Proceedings AAAI Conference on Artificial Intelligence, vol. 33, no. 01, pp. 3027\u20133035 (2019)","DOI":"10.1609\/aaai.v33i01.33013027"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Lewis, M.,et al.: 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":"5_CR14","doi-asserted-by":"crossref","unstructured":"Rajpurkar, P., Jia, R., Liang, P.:Know What You Don\u2019t Know: unanswerable questions for squad. arXiv preprint arXiv:1806.03822 (2018)","DOI":"10.18653\/v1\/P18-2124"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Hwang, J.D., et al.: (comet-) atomic 2020: on symbolic and neural commonsense knowledge graphs. In: Proceedings AAAI Conference Artificial Intelligence, vol. 35, no. 7, pp. 6384\u20136392 (2021)","DOI":"10.1609\/aaai.v35i7.16792"},{"key":"5_CR16","unstructured":"Roberts, A., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer (2019)"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Jin, M., Aletras, N.: Modeling the severity of complaints in social media. arXiv preprint arXiv:2103.12428 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.180"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-33380-4_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:53:39Z","timestamp":1710359619000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-33380-4_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031333798","9783031333804"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-33380-4_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"27 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Osaka","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"25 May 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 May 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2023.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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"813","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":"18% - 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":"3.5","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":"10","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)"}}]}}