{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,23]],"date-time":"2025-11-23T18:44:26Z","timestamp":1763923466180,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030736958"},{"type":"electronic","value":"9783030736965"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-73696-5_22","type":"book-chapter","created":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T07:03:23Z","timestamp":1617865403000},"page":"236-243","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Task Adaptive Pretraining of Transformers for Hostility Detection"],"prefix":"10.1007","author":[{"given":"Tathagata","family":"Raha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sayar","family":"Ghosh Roy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ujwal","family":"Narayan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zubair","family":"Abid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vasudeva","family":"Varma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,9]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","unstructured":"Badjatiya, P., Gupta, M., Varma, V.: Stereotypical bias removal for hate speech detection task using knowledge-based generalizations. In: The World Wide Web Conference, WWW 2019, pp. 49\u201359. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3308558.3313504","DOI":"10.1145\/3308558.3313504"},{"key":"22_CR2","doi-asserted-by":"publisher","unstructured":"Badjatiya, P., Gupta, S., Gupta, M., Varma, V.: Deep learning for hate speech detection in tweets. In: Proceedings of the 26th International Conference on World Wide Web Companion, WWW 2017 Companion, pp. 759\u2013760. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva (2017). https:\/\/doi.org\/10.1145\/3041021.3054223","DOI":"10.1145\/3041021.3054223"},{"key":"22_CR3","unstructured":"Bhardwaj, M., Akhtar, M.S., Ekbal, A., Das, A., Chakraborty, T.: Hostility detection dataset in Hindi (2020)"},{"key":"22_CR4","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":"22_CR5","unstructured":"Eisner, B., Rockt\u00e4schel, T., Augenstein, I., Bosnjak, M., Riedel, S.: emoji2vec: learning Emoji representations from their description. CoRR abs\/1609.08359 (2016). http:\/\/arxiv.org\/abs\/1609.08359"},{"key":"22_CR6","unstructured":"Ghosh Roy, S., Narayan, U., Raha, T., Abid, Z., Varma, V.: Leveraging multilingual transformers for hate speech detection. In: Working Notes of FIRE 2020 - Forum for Information Retrieval Evaluation. CEUR (2021)"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Gururangan, S., et al.: Don\u2019t stop pretraining: adapt language models to domains and tasks. arXiv preprint arXiv:2004.10964 (2020)","DOI":"10.18653\/v1\/2020.acl-main.740"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Kakwani, D., et al.: IndicNLPSuite: monolingual corpora, evaluation benchmarks and pre-trained multilingual language models for Indian languages. In: Findings of EMNLP (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.445"},{"key":"22_CR9","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017)"},{"key":"22_CR10","unstructured":"Kumar, R., Ojha, A.K., Zampieri, M., Malmasi, S. (eds.): Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018). Association for Computational Linguistics, Santa Fe, August 2018. https:\/\/www.aclweb.org\/anthology\/W18-4400"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Mandl, T., et al.: Overview of the HASOC track at FIRE 2020: hate speech and offensive content identification in Indo-European languages). In: Working Notes of FIRE 2020 - Forum for Information Retrieval Evaluation. CEUR, December 2020","DOI":"10.1145\/3441501.3441517"},{"key":"22_CR12","doi-asserted-by":"publisher","unstructured":"Mathew, B., Dutt, R., Goyal, P., Mukherjee, A.: Spread of hate speech in online social media. In: Proceedings of the 10th ACM Conference on Web Science, pp. 173\u2013182, June 2019. https:\/\/doi.org\/10.1145\/3292522.3326034","DOI":"10.1145\/3292522.3326034"},{"key":"22_CR13","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library (2019)"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Patwa, P., et al.: Overview of CONSTRAINT 2021 shared tasks: detecting English COVID-19 fake news and Hindi hostile posts. In: Chakraborty, T., et al. (eds.) CONSTRAINT 2021. CCIS, vol. 1402, pp. 42\u201353. Springer, Cham (2021)","DOI":"10.1007\/978-3-030-73696-5_5"},{"key":"22_CR15","unstructured":"Pinnaparaju, N., Indurthi, V., Varma, V.: Identifying fake news spreaders in social media. In: Cappellato, L., Eickhoff, C., Ferro, N., N\u00e9v\u00e9ol, A. (eds.) CLEF 2020 Labs and Workshops, Notebook Papers. CEUR-WS.org, September 2020"},{"key":"22_CR16","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(56), 1929\u20131958 (2014). http:\/\/jmlr.org\/papers\/v15\/srivastava14a.html"},{"key":"22_CR17","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"22_CR18","doi-asserted-by":"publisher","unstructured":"Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146\u20131151 (2018). https:\/\/doi.org\/10.1126\/science.aap9559. https:\/\/science.sciencemag.org\/content\/359\/6380\/1146","DOI":"10.1126\/science.aap9559"}],"container-title":["Communications in Computer and Information Science","Combating Online Hostile Posts in Regional Languages during Emergency Situation"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-73696-5_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T10:10:55Z","timestamp":1619863855000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-73696-5_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030736958","9783030736965"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-73696-5_22","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CONSTRAINT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on \u200bCombating On\u200bline Ho\u200bst\u200bile Posts in \u200bRegional Languages dur\u200bing Emerge\u200bncy Si\u200btuation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 February 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 February 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"constraint2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/lcs2.iiitd.edu.in\/CONSTRAINT-2021\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"62","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":"14","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":"9","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","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":"3.4","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)"}}]}}