{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T23:24:59Z","timestamp":1743117899279,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030938413"},{"type":"electronic","value":"9783030938420"}],"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\/978-3-030-93842-0_9","type":"book-chapter","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T18:25:30Z","timestamp":1641925530000},"page":"155-169","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dutch SQuAD and\u00a0Ensemble Learning for\u00a0Question Answering from\u00a0Labour Agreements"],"prefix":"10.1007","author":[{"given":"Niels J.","family":"Rouws","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Svitlana","family":"Vakulenko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sophia","family":"Katrenko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"9_CR1","doi-asserted-by":"crossref","unstructured":"Abadani, N., Mozafari, J., Fatemi, A., Nematbakhsh, M.A., Kazemi, A.: ParSQuAD: machine translated squad dataset for Persian question answering. In: 2021 7th International Conference on Web Research (ICWR), pp. 163\u2013168. IEEE (2021)","DOI":"10.1109\/ICWR51868.2021.9443126"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Aniol, A., Pietron, M., Duda, J.: Ensemble approach for natural language question answering problem. In: 2019 Seventh International Symposium on Computing and Networking Workshops (CANDARW), pp. 180\u2013183. IEEE (2019)","DOI":"10.1109\/CANDARW.2019.00039"},{"key":"9_CR3","unstructured":"Borzymowski, H.: Henryk\/BERT-base-multilingual-cased-finetuned-dutch-squad2 $$\\cdot $$ Hugging Face (2020). https:\/\/huggingface.co\/henryk\/bert-base-multilingual-cased-finetuned-dutch-squad2"},{"key":"9_CR4","unstructured":"Carrino, C.P., Costa-juss\u00e0, M.R., Fonollosa, J.A.R.: Automatic Spanish translation of the squad dataset for multilingual question answering. arXiv preprint arXiv:1912.05200 (2019)"},{"key":"9_CR5","unstructured":"de Vries, W., van Cranenburgh, A., Bisazza, A., Caselli, T., van Noord, G., Nissim, M.: BERTje: a Dutch BERT model. CoRR abs\/1912.09582 (2019). http:\/\/arxiv.org\/abs\/1912.09582"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Delobelle, P., Winters, T., Berendt, B.: RobBERT: a Dutch RoBERTa-based language model (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.292"},{"key":"9_CR7","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding (2019)"},{"key":"9_CR8","unstructured":"Hazen, T.J., Dhuliawala, S., Boies, D.: Towards domain adaptation from limited data for question answering using deep neural networks (2019)"},{"key":"9_CR9","unstructured":"Isotalo, L.: Generative question answering in a low-resource setting"},{"key":"9_CR10","unstructured":"Jeong, M., et al.: Transferability of natural language inference to biomedical question answering. arXiv preprint arXiv:2007.00217 (2020)"},{"issue":"4","key":"9_CR11","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee, J., et al.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234\u20131240 (2020)","journal-title":"Bioinformatics"},{"key":"9_CR12","unstructured":"Lee, K., Yoon, K., Park, S., Hwang, S.-W.: Semi-supervised training data generation for multilingual question answering. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) (2018)"},{"key":"9_CR13","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach (2019)"},{"key":"9_CR14","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization (2019)"},{"key":"9_CR15","unstructured":"Lui, M., Baldwin, T.: langid.py: an off-the-shelf language identification tool. In: Proceedings of the ACL 2012 System Demonstrations, Jeju Island, Korea. Association for Computational Linguistics, pp. 25\u201330, July 2012. https:\/\/www.aclweb.org\/anthology\/P12-3005"},{"key":"9_CR16","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013)"},{"key":"9_CR17","unstructured":"M\u00f6ller, T., Reina, A., Jayakumar, R., Pietsch, M.: COVID-QA: a question answering dataset for COVID-19. In: Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020 (2020)"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Pires, T., Schlinger, E., Garrette, D.: How multilingual is multilingual BERT? (2019)","DOI":"10.18653\/v1\/P19-1493"},{"key":"9_CR19","doi-asserted-by":"publisher","unstructured":"Poerner, N., Waltinger, U., Sch\u00fctze, H.: Inexpensive domain adaptation of pretrained language models: case studies on biomedical NER and COVID-19 QA. In: Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, pp. 1482\u20131490, November 2020. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.134, https:\/\/www.aclweb.org\/anthology\/2020.findings-emnlp.134","DOI":"10.18653\/v1\/2020.findings-emnlp.134"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: SQuAD: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250 (2016)","DOI":"10.18653\/v1\/D16-1264"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Rajpurkar, P., Jia, R., Liang, P.: Know what you don\u2019t know: unanswerable questions for SQuAD (2018)","DOI":"10.18653\/v1\/P18-2124"},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Rogers, A., Kovaleva, O., Rumshisky, A.: A primer in bertology: what we know about how BERT works (2020)","DOI":"10.1162\/tacl_a_00349"},{"key":"9_CR23","unstructured":"Startup in Residence Intergov. Geautomatiseerde tekst-analyse cao\u2019s | Startup in Residence Intergov (2020). https:\/\/intergov.startupinresidence.com\/nl\/szw\/geautomatiseerde-tekst-analyse-cao\/brief"},{"issue":"1","key":"9_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-015-0564-6","volume":"16","author":"G Tsatsaronis","year":"2015","unstructured":"Tsatsaronis, G., et al.: An overview of the bioASQ large-scale biomedical semantic indexing and question answering competition. BMC Bioinform. 16(1), 1\u201328 (2015)","journal-title":"BMC Bioinform."},{"key":"9_CR25","unstructured":"Vaswani, A., et al.: Attention is all you need (2017)"},{"key":"9_CR26","doi-asserted-by":"publisher","unstructured":"Williams, A., Nangia, N., Bowman, S.: A broad-coverage challenge corpus for sentence understanding through inference. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), New Orleans, Louisiana. Association for Computational Linguistics, pp. 1112\u20131122, June 2018. https:\/\/doi.org\/10.18653\/v1\/N18-1101, https:\/\/www.aclweb.org\/anthology\/N18-1101","DOI":"10.18653\/v1\/N18-1101"},{"key":"9_CR27","unstructured":"Xu, Y., Qiu, X., Zhou, L., Huang, X.: Improving BERT fine-tuning via self-ensemble and self-distillation. arXiv preprint arXiv:2002.10345 (2020)"}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence and Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-93842-0_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T18:04:59Z","timestamp":1700071499000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93842-0_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030938413","9783030938420"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93842-0_9","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BNAIC\/Benelearn","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Benelux Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Esch-sur-Alzette","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Luxembourg","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"33","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bnaic2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bnaic2021.uni.lu\/","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":"46","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":"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":"30% - 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":"2","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)"}}]}}