{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T07:33:17Z","timestamp":1771486397738,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031209796","type":"print"},{"value":"9783031209802","type":"electronic"}],"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\/978-3-031-20980-2_47","type":"book-chapter","created":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T19:03:09Z","timestamp":1668279789000},"page":"554-566","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Personalizing Retrieval-Based Dialogue Agents"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9442-8021","authenticated-orcid":false,"given":"Pavel","family":"Posokhov","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2935-991X","authenticated-orcid":false,"given":"Anastasia","family":"Matveeva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8992-9654","authenticated-orcid":false,"given":"Olesia","family":"Makhnytkina","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7494-8329","authenticated-orcid":false,"given":"Anton","family":"Matveev","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7010-1585","authenticated-orcid":false,"given":"Yuri","family":"Matveev","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,10]]},"reference":[{"key":"47_CR1","unstructured":"Anaby-Tavor, A., Carmeli, B., Goldbraich, E., Kantor, A., Kour, G., Shlomov, S., Tepper, N., Zwerdling, N.: Not enough data? deep learning to the rescue! (2019). http:\/\/arxiv.org\/abs\/1911.03118"},{"key":"47_CR2","doi-asserted-by":"crossref","unstructured":"Andreas, J.: Good-enough compositional data augmentation (2019). http:\/\/arxiv.org\/abs\/1904.09545","DOI":"10.18653\/v1\/2020.acl-main.676"},{"key":"47_CR3","doi-asserted-by":"publisher","unstructured":"Chalkidis, I., Androutsopoulos, I., Michos, A.: Extracting contract elements. In: Proceedings of the 16th Edition of the International Conference on Articial Intelligence and Law, pp. 19\u201328. ICAIL \u201917, Association for Computing Machinery, New York, NY, USA (2017). https:\/\/doi.org\/10.1145\/3086512.3086515","DOI":"10.1145\/3086512.3086515"},{"key":"47_CR4","unstructured":"Coulombe, C.: Text data augmentation made simple by leveraging NLP cloud apis (2018). http:\/\/arxiv.org\/abs\/1812.04718"},{"key":"47_CR5","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol.1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423, https:\/\/aclanthology.org\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"47_CR6","doi-asserted-by":"publisher","unstructured":"Edunov, S., Ott, M., Auli, M., Grangier, D.: Understanding back-translation at scale. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 489\u2013500. Association for Computational Linguistics, Brussels, Belgium (2018). https:\/\/doi.org\/10.18653\/v1\/D18-1045, https:\/\/aclanthology.org\/D18-1045","DOI":"10.18653\/v1\/D18-1045"},{"issue":"4","key":"47_CR7","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/s11023-020-09548-1","volume":"30","author":"L Floridi","year":"2020","unstructured":"Floridi, L., Chiriatti, M.: GPT-3: its nature, scope, limits, and consequences. Mind. Mach. 30(4), 681\u2013694 (2020). https:\/\/doi.org\/10.1007\/s11023-020-09548-1","journal-title":"Mind. Mach."},{"key":"47_CR8","doi-asserted-by":"crossref","unstructured":"Giridhara, P.K.B., Mishra, C., Venkataramana, R.K.M., Bukhari, S.S., Dengel, A.R.: A study of various text augmentation techniques for relation classification in free text. In: ICPRAM (2019)","DOI":"10.5220\/0007311003600367"},{"key":"47_CR9","unstructured":"Guo, H., Mao, Y., Zhang, R.: Augmenting data with mixup for sentence classification: an empirical study (2019). arXiv:abs\/1905.08941"},{"key":"47_CR10","doi-asserted-by":"publisher","unstructured":"Hancock, B., Bordes, A., Mazare, P.E., Weston, J.: Learning from dialogue after deployment: feed yourself, chatbot! pp. 3667\u20133684 (2019). https:\/\/doi.org\/10.18653\/v1\/P19-1358","DOI":"10.18653\/v1\/P19-1358"},{"key":"47_CR11","unstructured":"Humeau, S., Shuster, K., Lachaux, M., Weston, J.: Real-time inference in multi-sentence tasks with deep pretrained transformers (2019). http:\/\/arxiv.org\/abs\/1905.01969"},{"key":"47_CR12","unstructured":"Humeau, S., Shuster, K., Lachaux, M.A., Weston, J.: Poly-encoders: architectures and pre-training strategies for fast and accurate multi-sentence scoring. In: International Conference on Learning Representations (2020). https:\/\/openreview.net\/forum?id=SkxgnnNFvH"},{"key":"47_CR13","doi-asserted-by":"publisher","unstructured":"Iyyer, M., Wieting, J., Gimpel, K., Zettlemoyer, L.: Adversarial example generation with syntactically controlled paraphrase networks. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers), pp. 1875\u20131885. Association for Computational Linguistics, New Orleans, Louisiana (2018). https:\/\/doi.org\/10.18653\/v1\/N18-1170, https:\/\/aclanthology.org\/N18-1170","DOI":"10.18653\/v1\/N18-1170"},{"key":"47_CR14","doi-asserted-by":"crossref","unstructured":"Kobayashi, S.: Contextual augmentation: data augmentation by words with paradigmatic relations (2018). arXiv:abs\/1805.06201","DOI":"10.18653\/v1\/N18-2072"},{"key":"47_CR15","unstructured":"Kumar, V., Choudhary, A., Cho, E.: Data augmentation using pre-trained transformer models (2020). arXiv:abs\/2003.02245"},{"key":"47_CR16","doi-asserted-by":"publisher","unstructured":"Lin, Z., Liu, Z., Winata, G.I., Cahyawijaya, S., Madotto, A., Bang, Y., Ishii, E., Fung, P.: XPersona: evaluating multilingual personalized chatbot. In: Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI. pp. 102\u2013112. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.nlp4convai-1.10, https:\/\/aclanthology.org\/2021.nlp4convai-1.10","DOI":"10.18653\/v1\/2021.nlp4convai-1.10"},{"key":"47_CR17","doi-asserted-by":"publisher","unstructured":"Matveev, A., Makhnytkina, O., Matveev, Y., Svischev, A., Korobova, P., Rybin, A., Akulov, A.: Virtual dialogue assistant for remote exams. Mathematics 9(18) (2021). https:\/\/doi.org\/10.3390\/math9182229, https:\/\/www.mdpi.com\/2227-7390\/9\/18\/2229","DOI":"10.3390\/math9182229"},{"key":"47_CR18","doi-asserted-by":"crossref","unstructured":"Ni, J., Young, T., Pandelea, V., Xue, F., Adiga, V., Cambria, E.: Recent advances in deep learning-based dialogue systems (2021)","DOI":"10.1007\/s10462-022-10248-8"},{"key":"47_CR19","unstructured":"Papadaki, M., Chalkidis, I., Michos, A.: Data augmentation techniques for legal text analytics (2017)"},{"key":"47_CR20","doi-asserted-by":"publisher","unstructured":"Posokhov, P., Apanasovich, K., Matveeva, A., Makhnytkina, O., Matveev, A.: Personalizing dialogue agents for Russian: retrieve and refine, vol. 2022, pp. 245\u2013252 (2022). https:\/\/doi.org\/10.23919\/FRUCT54823.2022.9770895","DOI":"10.23919\/FRUCT54823.2022.9770895"},{"key":"47_CR21","doi-asserted-by":"publisher","unstructured":"Roller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, Y., Xu, J., Ott, M., Smith, E.M., Boureau, Y.L., Weston, J.: Recipes for building an open-domain chatbot. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 300\u2013325. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.eacl-main.24, https:\/\/aclanthology.org\/2021.eacl-main.24","DOI":"10.18653\/v1\/2021.eacl-main.24"},{"key":"47_CR22","unstructured":"Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. CoRR abs\/1508.07909 (2015), http:\/\/arxiv.org\/abs\/1508.07909"},{"key":"47_CR23","unstructured":"Shen, T., Lei, T., Barzilay, R., Jaakkola, T.S.: Style transfer from non-parallel text by cross-alignment (2017). arXiv:abs\/1705.09655"},{"key":"47_CR24","unstructured":"Sugiyama, H., Mizukami, M., Arimoto, T., Narimatsu, H., Chiba, Y., Nakajima, H., Meguro, T.: Empirical analysis of training strategies of transformer-based Japanese chit-chat systems (2021). arXiv:abs\/2109.05217"},{"key":"47_CR25","doi-asserted-by":"crossref","unstructured":"Wei, J.W., Zou, K.: EDA: easy data augmentation techniques for boosting performance on text classification tasks (2019). arXiv:abs\/1901.11196","DOI":"10.18653\/v1\/D19-1670"},{"key":"47_CR26","doi-asserted-by":"publisher","unstructured":"Wu, X., Xia, Y., Zhu, J., Wu, L., Xie, S., Fan, Y., Qin, T.: Mixseq: a simple data augmentation method for neural machine translation, pp. 192\u2013197 (2021). https:\/\/doi.org\/10.18653\/v1\/2021.iwslt-1.23","DOI":"10.18653\/v1\/2021.iwslt-1.23"},{"key":"47_CR27","unstructured":"Yang, Z., Hu, Z., Dyer, C., Xing, E.P., Berg-Kirkpatrick, T.: Unsupervised text style transfer using language models as discriminators (2018). arXiv:abs\/1805.11749"},{"key":"47_CR28","doi-asserted-by":"publisher","unstructured":"Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., Weston, J.: Personalizing dialogue agents: I have a dog, do you have pets too? In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 2204\u20132213. Association for Computational Linguistics, Melbourne, Australia (2018). https:\/\/doi.org\/10.18653\/v1\/P18-1205, https:\/\/aclanthology.org\/P18-1205","DOI":"10.18653\/v1\/P18-1205"},{"key":"47_CR29","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Zweigenbaum, P.: Gneg: graph-based negative sampling for word2vec (2018). https:\/\/doi.org\/10.18653\/v1\/P18-2090","DOI":"10.18653\/v1\/P18-2090"},{"key":"47_CR30","unstructured":"Zhong, P., Sun, Y., Liu, Y., Zhang, C., Wang, H., Nie, Z., Miao, C.: Endowing empathetic dialogue systems with personas (2020). arXiv:abs\/2004.12316"}],"container-title":["Lecture Notes in Computer Science","Speech and Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20980-2_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T19:09:31Z","timestamp":1668280171000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20980-2_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031209796","9783031209802"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20980-2_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"10 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SPECOM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Speech and Computer","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gurugram","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":"14 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"specom2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.specom.co.in","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":"99","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":"60","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":"61% - 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":"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)"}}]}}