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Kurzweil, \u201cConversational contextual cues: The case of personalization and history for response ranking,\u201d CoRR, abs\/1606.00372, 2016."},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] M. Nakatsuji and S. Okui, \u201cAnswer generation through unified memories over multiple passages,\u201d IJCAI, pp.3823-3829, ijcai.org, 2020. 10.24963\/ijcai.2020\/529","DOI":"10.24963\/ijcai.2020\/529"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] Q. Liu, Y. Chen, B. Chen, J.-G. Lou, Z. Chen, B. Zhou, and D. Zhang, \u201cYou impress me: Dialogue generation via mutual persona perception,\u201d Proc. ACL\u201920, pp.1417-1427, 2020. 10.18653\/v1\/2020.acl-main.131","DOI":"10.18653\/v1\/2020.acl-main.131"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] S. Zhang, E. Dinan, J. Urbanek, A. Szlam, D. Kiela, and J. Weston, \u201cPersonalizing dialogue agents: I have a dog, do you have pets too?,\u201d Proc. ACL\u201918, pp.2204-2213, 2018. 10.18653\/v1\/p18-1205","DOI":"10.18653\/v1\/P18-1205"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] Y. Liu, J. Du, X. Li, and R. Xu, \u201cGenerating empathetic responses by injecting anticipated emotion,\u201d Proc. ICASSP\u201921, 2021. 10.1109\/icassp39728.2021.9413596","DOI":"10.1109\/ICASSP39728.2021.9413596"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] H. Song, W.-N. Zhang, J. Hu, and T. Liu, \u201cGenerating persona consistent dialogues by exploiting natural language inference,\u201d Proc. AAAI\u201920, pp.8878-8885, 2020. 10.1609\/aaai.v34i05.6417","DOI":"10.1609\/aaai.v34i05.6417"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] M. Henderson, I. Casanueva, N. Mrk\u0161i\u0107, P.-H. Su, T.-H. Wen, and I. Vuli\u0107, \u201cConveRT: Efficient and accurate conversational representations from transformers,\u201d Proc. EMNLP\u201920, pp.2161-2174, 2020. 10.18653\/v1\/2020.findings-emnlp.196","DOI":"10.18653\/v1\/2020.findings-emnlp.196"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] C. Xu, P. Li, W. Wang, H. Yang, S. Wang, and C. Xiao, \u201cCOSPLAY: concept set guided personalized dialogue generation across both party personas,\u201d Proc. SIGIR\u201922, pp.201-211, 2022. 10.1145\/3477495.3531957","DOI":"10.1145\/3477495.3531957"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] H. Lu, W. Lam, H. Cheng, and H. Meng, \u201cPartner personas generation for dialogue response generation,\u201d Proc. NAACL\u201922, pp.5200-5212, 2022. 10.18653\/v1\/2022.naacl-main.382","DOI":"10.18653\/v1\/2022.naacl-main.382"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] K. Cho, B. van Merri\u00ebnboer, D. Bahdanau, and Y. Bengio, \u201cOn the properties of neural machine translation: Encoder-decoder approaches,\u201d Proc. SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pp.103-111, 2014. 10.3115\/v1\/w14-4012","DOI":"10.3115\/v1\/W14-4012"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] M. Henderson, I. Vuli\u0107, D. Gerz, I. Casanueva, P. Budzianowski, S. Coope, G. Spithourakis, T.-H. Wen, N. Mrk\u0161i\u0107, and P.-H. Su, \u201cTraining neural response selection for task-oriented dialogue systems,\u201d Proc. ACL\u201919, pp.5392-5404, 2019. 10.18653\/v1\/p19-1536","DOI":"10.18653\/v1\/P19-1536"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] T. Whang, D. Lee, C. Lee, K. Yang, D. Oh, and H. Lim, \u201cAn effective domain adaptive post-training method for BERT in response selection,\u201d Proc. Interspeech\u201920, pp.1585-1589, 2020. 10.21437\/interspeech.2020-2153","DOI":"10.21437\/Interspeech.2020-2153"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] H. Zhang, Y. Lan, L. Pang, H. Chen, Z. Ding, and D. Yin, \u201cModeling topical relevance for multi-turn dialogue generation,\u201d Proc. IJCAI\u201920, pp.3737-3743, 2020. 10.24963\/ijcai.2020\/517","DOI":"10.24963\/ijcai.2020\/517"},{"key":"17","unstructured":"[17] M. Hardalov, I. Koychev, and P. Nakov, \u201cEnriched pre-trained transformers for joint slot filling and intent detection,\u201d CoRR, abs\/2004.14848, 2020."},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] A. Rastogi, X. Zang, S. Sunkara, R. Gupta, and P. Khaitan, \u201cTowards scalable multi-domain conversational agents: The schema-guided dialogue dataset,\u201d Proc. AAAI\u201920, vol.34, no.05, pp.8689-8696, 2020. 10.1609\/aaai.v34i05.6394","DOI":"10.1609\/aaai.v34i05.6394"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] J. Wang, J. Liu, W. Bi, X. Liu, K. He, R. Xu, and M. Yang, \u201cImproving knowledge-aware dialogue generation via knowledge base question answering,\u201d Proc. AAAI\u201920, vol.34, no.05, pp.9169-9176, 2020. 10.1609\/aaai.v34i05.6453","DOI":"10.1609\/aaai.v34i05.6453"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] M. Nakatsuji and S. Okui, \u201cConclusion-supplement answer generation for non-factoid questions,\u201d Proc. AAAI\u201920, vol.34, no.05, pp.8520-8527, 2020. 10.1609\/aaai.v34i05.6373","DOI":"10.1609\/aaai.v34i05.6373"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] L. Chen, Y. Zhao, B. Lyu, L. Jin, Z. Chen, S. Zhu, and K. Yu, \u201cNeural graph matching networks for chinese short text matching,\u201d Proc. ACL\u201920, pp.6152-6158, 2020. 10.18653\/v1\/2020.acl-main.547","DOI":"10.18653\/v1\/2020.acl-main.547"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] Y. Xu, H. Zhao, and Z. Zhang, \u201cTopic-aware multi-turn dialogue modeling,\u201d Proc. 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