{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T15:06:10Z","timestamp":1743001570747,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031821493"},{"type":"electronic","value":"9783031821509"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-82150-9_1","type":"book-chapter","created":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T21:14:32Z","timestamp":1740863672000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ReaL: Reassessing Softmax Score by\u00a0Logits Distillation for\u00a0Foundation Model Based Intent Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5781-1088","authenticated-orcid":false,"given":"G\u00e1bor","family":"Sz\u0171cs","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2051-4036","authenticated-orcid":false,"given":"Modafar","family":"Al-Shouha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,2]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","unstructured":"Al-Shouha, M., Sz\u0171cs, G.: Single and combined algorithms for open set classification on image datasets. Acta Cybernetica (2024). https:\/\/doi.org\/10.14232\/actacyb.298356, https:\/\/cyber.bibl.u-szeged.hu\/index.php\/actcybern\/article\/view\/4324","DOI":"10.14232\/actacyb.298356"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Bendale, A., Boult, T.E.: Towards open set deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1563\u20131572 (2016)","DOI":"10.1109\/CVPR.2016.173"},{"issue":"2","key":"1_CR3","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1145\/3166054.3166058","volume":"19","author":"H Chen","year":"2017","unstructured":"Chen, H., Liu, X., Yin, D., Tang, J.: A survey on dialogue systems: recent advances and new frontiers. ACM SIGKDD Explor. Newsl. 19(2), 25\u201335 (2017). https:\/\/doi.org\/10.1145\/3166054.3166058","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"1_CR4","doi-asserted-by":"publisher","unstructured":"Conneau, A., et al.: Unsupervised cross-lingual representation learning at scale (2020). https:\/\/doi.org\/10.48550\/arXiv.1911.02116","DOI":"10.48550\/arXiv.1911.02116"},{"key":"1_CR5","doi-asserted-by":"publisher","unstructured":"Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q.V., Salakhutdinov, R.: Transformer-xl: attentive language models beyond a fixed-length context (2019). https:\/\/doi.org\/10.48550\/arXiv.1901.02860","DOI":"10.48550\/arXiv.1901.02860"},{"key":"1_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":"1_CR7","doi-asserted-by":"crossref","unstructured":"FitzGerald, J., et\u00a0al.: Massive: a 1 m-example multilingual natural language understanding dataset with 51 typologically-diverse languages. arXiv preprint arXiv:2204.08582 (2022)","DOI":"10.18653\/v1\/2023.acl-long.235"},{"key":"1_CR8","unstructured":"Larson, S., et\u00a0al.: An evaluation dataset for intent classification and out-of-scope prediction. arXiv preprint arXiv:1909.02027 (2019)"},{"key":"1_CR9","unstructured":"Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv preprint arXiv:1706.02690 (2017)"},{"key":"1_CR10","unstructured":"Ouyang, Y., Wu, Z., Dai, X., Huang, S., Chen, J.: Towards multi-label unknown intent detection. In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 626\u2013635 (2022). https:\/\/aclanthology.org\/2022.coling-1.52"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Shen, Y., Hsu, Y.C., Ray, A., Jin, H.: Enhancing the generalization for intent classification and out-of-domain detection in SLU. arXiv preprint arXiv:2106.14464 (2021)","DOI":"10.18653\/v1\/2021.acl-long.190"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Shu, L., Xu, H., Liu, B.: DOC: deep open classification of text documents. arXiv preprint arXiv:1709.08716 (2017)","DOI":"10.18653\/v1\/D17-1314"},{"key":"1_CR13","doi-asserted-by":"publisher","unstructured":"Wang, W., Wei, F., Dong, L., Bao, H., Yang, N., Zhou, M.: MiniLM: deep self-attention distillation for task-agnostic compression of pre-trained transformers (2020). https:\/\/doi.org\/10.48550\/arXiv.2002.10957","DOI":"10.48550\/arXiv.2002.10957"},{"key":"1_CR14","doi-asserted-by":"publisher","unstructured":"Yang, J., Zhou, K., Li, Y., Liu, Z.: Generalized out-of-distribution detection: a survey. arXiv preprint arXiv:2110.11334 (2021). https:\/\/doi.org\/10.48550\/arXiv.2110.11334","DOI":"10.48550\/arXiv.2110.11334"},{"key":"1_CR15","doi-asserted-by":"publisher","unstructured":"Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V.: XLNet: generalized autoregressive pretraining for language understanding (2020). https:\/\/doi.org\/10.48550\/arXiv.1906.08237","DOI":"10.48550\/arXiv.1906.08237"},{"key":"1_CR16","unstructured":"Zhan, L.M., Liang, H., Fan, L., Wu, X.M., Lam, A.Y.: A closer look at few-shot out-of-distribution intent detection. In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 451\u2013460 (2022). https:\/\/aclanthology.org\/2022.coling-1.36"},{"key":"1_CR17","doi-asserted-by":"publisher","unstructured":"Zhang, H., Xu, H., Lin, T.E.: Deep open intent classification with adaptive decision boundary. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 14374\u201314382 (2021).https:\/\/doi.org\/10.1609\/aaai.v35i16.17690","DOI":"10.1609\/aaai.v35i16.17690"},{"key":"1_CR18","doi-asserted-by":"publisher","unstructured":"Zhang, J., et al.: Are pretrained transformers robust in intent classification? A missing ingredient in evaluation of out-of-scope intent detection (2022). https:\/\/doi.org\/10.48550\/arXiv.2106.04564","DOI":"10.48550\/arXiv.2106.04564"},{"key":"1_CR19","doi-asserted-by":"publisher","unstructured":"Zhou, Y., Yang, J., Wang, P., Qiu, X.: Two birds one stone: dynamic ensemble for OOD intent classification. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 10659\u201310673. Association for Computational inguistics, Toronto, Canada (2023). https:\/\/doi.org\/10.18653\/v1\/2023.acl-long.595, https:\/\/aclanthology.org\/2023.acl-long.595","DOI":"10.18653\/v1\/2023.acl-long.595"}],"container-title":["Communications in Computer and Information Science","Intelligent Systems and Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-82150-9_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T21:14:38Z","timestamp":1740863678000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-82150-9_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031821493","9783031821509"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-82150-9_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2 March 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Systems and Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Istanbul","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"T\u00fcrkiye","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ispr22024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ispr2024.sciencesconf.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}