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However, knowledge redundancy and distraction of irrelevant dialogue content often exist in knowledge-grounded conversations, which may affect the matching process and lead to inferior performance. In addition, irrelevant dialogue history and excessive knowledge also hinder the exploitation of popular pre-trained language models (PLMs) due to the limitation of input length. To address these challenges, we propose a new knowledge-grounded dialogue model based on PLMs, where a knowledge selector and a context selector are designed for filtering out irrelevant knowledge sentences and redundant dialogue history, respectively. Considering the lack of labeled data for the learning of two selectors, we pre-train them with weakly-supervised tasks and then jointly conduct the optimization of knowledge and context selection and fine-tuning of PLMs for response ranking with reinforcement learning (RL). By this means, the dialogue model can distill more accurate and concise knowledge and dialogue content for subsequent response ranking module, and the overall model can converge and perform better. We conduct experiments on two benchmarks and evaluation results indicate that our model can significantly outperform the state-of-the-art methods.<\/jats:p>","DOI":"10.1145\/3584701","type":"journal-article","created":{"date-parts":[[2023,2,16]],"date-time":"2023-02-16T11:35:11Z","timestamp":1676547311000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Learning Multi-turn Response Selection in Grounded Dialogues with Reinforced Knowledge and Context Distillation"],"prefix":"10.1145","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5832-6199","authenticated-orcid":false,"given":"Jiazhan","family":"Feng","sequence":"first","affiliation":[{"name":"Wangxuan Institute of Computer Technology and School of Intelligence Science and Technology, Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4162-2119","authenticated-orcid":false,"given":"Chongyang","family":"Tao","sequence":"additional","affiliation":[{"name":"Microsoft, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9418-8881","authenticated-orcid":false,"given":"Xueliang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Wangxuan Institute of Computer Technology and Center for Data Science, AAIS, Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0396-6703","authenticated-orcid":false,"given":"Dongyan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Wangxuan Institute of Computer Technology, Peking University; National Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,4,21]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Daniel Adiwardana Minh-Thang Luong David R. So Jamie Hall Noah Fiedel Romal Thoppilan Zi Yang Apoorv Kulshreshtha Gaurav Nemade Yifeng Lu and Quoc V. Le. 2020. Towards a human-like open-domain chatbot. 10.48550\/ARXIV.2001.09977"},{"key":"e_1_3_2_3_2","first-page":"675","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics","author":"Chen Yen-Chun","year":"2018","unstructured":"Yen-Chun Chen and Mohit Bansal. 2018. Fast abstractive summarization with reinforce-selected sentence rewriting. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Melbourne, Australia, 675\u2013686. DOI:10.18653\/v1\/P18-1063"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"2256","DOI":"10.18653\/v1\/D16-1245","volume-title":"Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing","author":"Clark Kevin","year":"2016","unstructured":"Kevin Clark and Christopher D. 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DOI:10.18653\/v1\/N19-1423"},{"key":"e_1_3_2_6_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Dinan Emily","year":"2018","unstructured":"Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston. 2018. Wizard of Wikipedia: Knowledge-powered conversational agents. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_7_2","first-page":"1243","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Gehring Jonas","year":"2017","unstructured":"Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. 2017. Convolutional sequence to sequence learning. In Proceedings of the International Conference on Machine Learning. 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In Proceedings of the INTERSPEECH. 1891\u20131895."},{"key":"e_1_3_2_10_2","first-page":"2041","volume-title":"Proceedings of the 29th ACM International Conference on Information and Knowledge Management","author":"Gu Jia-Chen","year":"2020","unstructured":"Jia-Chen Gu, Tianda Li, Quan Liu, Zhen-Hua Ling, Zhiming Su, Si Wei, and Xiaodan Zhu. 2020. Speaker-aware BERT for multi-turn response selection in retrieval-based chatbots. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management. 2041\u20132044."},{"key":"e_1_3_2_11_2","first-page":"1412","volume-title":"Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020","author":"Gu Jia-Chen","year":"2020","unstructured":"Jia-Chen Gu, Zhenhua Ling, Quan Liu, Zhigang Chen, and Xiaodan Zhu. 2020. Filtering before iteratively referring for knowledge-grounded response selection in retrieval-based chatbots. 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DOI:10.18653\/v1\/D19-1193"},{"key":"e_1_3_2_13_2","first-page":"1549","volume-title":"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Han Janghoon","year":"2021","unstructured":"Janghoon Han, Taesuk Hong, Byoungjae Kim, Youngjoong Ko, and Jungyun Seo. 2021. Fine-grained post-training for improving retrieval-based dialogue systems. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online, 1549\u20131558. DOI:10.18653\/v1\/2021.naacl-main.122"},{"key":"e_1_3_2_14_2","doi-asserted-by":"crossref","first-page":"5392","DOI":"10.18653\/v1\/P19-1536","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Henderson Matthew","year":"2019","unstructured":"Matthew Henderson, Ivan Vuli\u0107, Daniela Gerz, I\u00f1igo Casanueva, Pawe\u0142 Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrk\u0161i\u0107, and Pei-Hao Su. 2019. Training neural response selection for task-oriented dialogue systems. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 5392\u20135404. DOI:10.18653\/v1\/P19-1536"},{"key":"e_1_3_2_15_2","unstructured":"Baotian Hu Zhengdong Lu Hang Li and Qingcai Chen. 2014. Convolutional neural network architectures for matching natural language sentences. In Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS\u201914 Montreal Canada) Volume 2 MIT Press Cambridge MA 2042\u20132050."},{"key":"e_1_3_2_16_2","first-page":"525","volume-title":"Proceedings of the 29th ACM International Conference on Information and Knowledge Management","author":"Hua Kai","year":"2020","unstructured":"Kai Hua, Zhiyuan Feng, Chongyang Tao, Rui Yan, and Lu Zhang. 2020. Learning to detect relevant contexts and knowledge for response selection in retrieval-based dialogue systems. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management. 525\u2013534."},{"key":"e_1_3_2_17_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Humeau Samuel","year":"2019","unstructured":"Samuel Humeau, Kurt Shuster, Marie-Anne Lachaux, and Jason Weston. 2019. Poly-encoders: Architectures and pre-training strategies for fast and accurate multi-sentence scoring. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_18_2","unstructured":"Zongcheng Ji Zhengdong Lu and Hang Li. 2014. An information retrieval approach to short text conversation. arXiv:1408.6988. Retrieved from https:\/\/arxiv.org\/abs\/1408.6988."},{"key":"e_1_3_2_19_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Kingma Diederik P.","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for Stochastic Optimization. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_20_2","unstructured":"Feng-Lin Li Minghui Qiu Haiqing Chen Xiongwei Wang Xing Gao Jun Huang Juwei Ren Zhongzhou Zhao Weipeng Zhao Lei Wang Guwei Jin and Wei Chu. 2017. AliMe Assist: An intelligent assistant for creating an innovative e-commerce experience. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM\u201917 Singapore Singapore) . Association for Computing Machinery New York NY 2495\u20132498. 10.1145\/3132847.3133169"},{"key":"e_1_3_2_21_2","first-page":"110","volume-title":"Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Li Jiwei","year":"2016","unstructured":"Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016. A diversity-promoting objective function for neural conversation models. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, San Diego, California, 110\u2013119. 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Roberta: A robustly optimized BERT pretraining approach. 10.48550\/ARXIV.1907.11692"},{"key":"e_1_3_2_26_2","doi-asserted-by":"crossref","first-page":"285","DOI":"10.18653\/v1\/W15-4640","volume-title":"Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue","author":"Lowe Ryan","year":"2015","unstructured":"Ryan Lowe, Nissan Pow, Iulian Serban, and Joelle Pineau. 2015. The Ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems. In Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Association for Computational Linguistics, Prague, Czech Republic, 285\u2013294. DOI:10.18653\/v1\/W15-4640"},{"key":"e_1_3_2_27_2","doi-asserted-by":"crossref","first-page":"2775","DOI":"10.18653\/v1\/D18-1298","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","author":"Mazar\u00e9 Pierre-Emmanuel","year":"2018","unstructured":"Pierre-Emmanuel Mazar\u00e9, Samuel Humeau, Martin Raison, and Antoine Bordes. 2018. Training millions of personalized dialogue agents. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, Belgium, 2775\u20132779. DOI:10.18653\/v1\/D18-1298"},{"key":"e_1_3_2_28_2","first-page":"3349","volume-title":"Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers","author":"Mou Lili","year":"2016","unstructured":"Lili Mou, Yiping Song, Rui Yan, Ge Li, Lu Zhang, and Zhi Jin. 2016. Sequence to backward and forward sequences: A content-introducing approach to generative short-text conversation. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. The COLING 2016 Organizing Committee, Osaka, Japan, 3349\u20133358. Retrieved from https:\/\/aclanthology.org\/C16-1316."},{"key":"e_1_3_2_29_2","unstructured":"OpenAI. 2022. ChatGPT: Optimizing language models for dialogue. Retrieved from https:\/\/openai.com\/blog\/chatgpt\/."},{"key":"e_1_3_2_30_2","first-page":"311","volume-title":"Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics","author":"Papineni Kishore","year":"2002","unstructured":"Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: A method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. 311\u2013318. DOI:10.3115\/1073083.1073135"},{"key":"e_1_3_2_31_2","first-page":"8026","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An imperative style, high-performance deep learning library. In Proceedings of the Advances in Neural Information Processing Systems 32. H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.), Curran Associates, Inc., 8026\u20138037. Retrieved from http:\/\/papers.nips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf."},{"key":"e_1_3_2_32_2","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.18653\/v1\/D16-1264","volume-title":"Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing","author":"Rajpurkar Pranav","year":"2016","unstructured":"Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Austin, Texas, 2383\u20132392. DOI:10.18653\/v1\/D16-1264"},{"key":"e_1_3_2_33_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Serban Iulian","year":"2016","unstructured":"Iulian Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau. 2016. Building end-to-end dialogue systems using generative hierarchical neural network models. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_34_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Serban Iulian","year":"2017","unstructured":"Iulian Serban, Alessandro Sordoni, Ryan Lowe, Laurent Charlin, Joelle Pineau, Aaron Courville, and Yoshua Bengio. 2017. A hierarchical latent variable encoder-decoder model for generating dialogues. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_35_2","first-page":"1577","volume-title":"Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing","author":"Shang Lifeng","year":"2015","unstructured":"Lifeng Shang, Zhengdong Lu, and Hang Li. 2015. Neural responding machine for short-text conversation. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Association for Computational Linguistics, Beijing, China, 1577\u20131586. DOI:10.3115\/v1\/P15-1152"},{"issue":"1","key":"e_1_3_2_36_2","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1631\/FITEE.1700826","article-title":"From Eliza to XiaoIce: Challenges and opportunities with social chatbots","volume":"19","author":"Shum Heung-Yeung","year":"2018","unstructured":"Heung-Yeung Shum, Xiao-dong He, and Di Li. 2018. From Eliza to XiaoIce: Challenges and opportunities with social chatbots. Frontiers of Information Technology and Electronic Engineering 19, 1 (2018), 10\u201326.","journal-title":"Frontiers of Information Technology and Electronic Engineering"},{"key":"e_1_3_2_37_2","first-page":"3104","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Proceedings of the Advances in Neural Information Processing Systems. 3104\u20133112."},{"key":"e_1_3_2_38_2","unstructured":"Richard S. Sutton David McAllester Satinder Singh and Yishay Mansour. 1999. Policy gradient methods for reinforcement learning with function approximation. In Proceedings of the 12th International Conference on Neural Information Processing Systems (NIPS\u201999 Denver CO) . MIT Press Cambridge MA 1057\u20131063."},{"key":"e_1_3_2_39_2","first-page":"4446","volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing","author":"Tao Chongyang","year":"2021","unstructured":"Chongyang Tao, Changyu Chen, Jiazhan Feng, Ji-Rong Wen, and Rui Yan. 2021. A pre-training strategy for zero-resource response selection in knowledge-grounded conversations. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. Association for Computational Linguistics, Online, 4446\u20134457. DOI:10.18653\/v1\/2021.acl-long.343"},{"key":"e_1_3_2_40_2","first-page":"267","volume-title":"Proceedings of the 12th ACM International Conference on Web Search and Data Mining.","author":"Tao Chongyang","year":"2019","unstructured":"Chongyang Tao, Wei Wu, Can Xu, Wenpeng Hu, Dongyan Zhao, and Rui Yan. 2019. Multi-representation fusion network for multi-turn response selection in retrieval-based chatbots. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining.Association for Computing Machinery, 267\u2013275. DOI:10.1145\/3289600.3290985"},{"key":"e_1_3_2_41_2","first-page":"1","volume-title":"Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics","author":"Tao Chongyang","year":"2019","unstructured":"Chongyang Tao, Wei Wu, Can Xu, Wenpeng Hu, Dongyan Zhao, and Rui Yan. 2019. One time of interaction may not be enough: Go deep with an interaction-over-interaction network for response selection in dialogues. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 1\u201311. DOI:10.18653\/v1\/P19-1001"},{"key":"e_1_3_2_42_2","unstructured":"Iulia Turc Ming-Wei Chang Kenton Lee and Kristina Toutanova. 2019. Well-read students learn better: On the importance of pre-training compact models. 10.48550\/ARXIV.1908.08962"},{"key":"e_1_3_2_43_2","article-title":"Attention is all you need","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_44_2","volume-title":"Proceedings of the Workshop on DSTC7","author":"Vig Jesse","year":"2019","unstructured":"Jesse Vig and Kalai Ramea. 2019. Comparison of transfer-learning approaches for response selection in multi-turn conversations. In Proceedings of the Workshop on DSTC7."},{"key":"e_1_3_2_45_2","unstructured":"Oriol Vinyals and Quoc Le. 2015. A neural conversational model. 10.48550\/ARXIV.1506.05869"},{"key":"e_1_3_2_46_2","first-page":"935","volume-title":"Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing","author":"Wang Hao","year":"2013","unstructured":"Hao Wang, Zhengdong Lu, Hang Li, and Enhong Chen. 2013. A dataset for research on short-text conversations. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 935\u2013945. Retrieved from https:\/\/aclanthology.org\/D13-1096."},{"key":"e_1_3_2_47_2","volume-title":"Proceedings of the 24th International Joint Conference on Artificial Intelligence","author":"Wang Mingxuan","year":"2015","unstructured":"Mingxuan Wang, Zhengdong Lu, Hang Li, and Qun Liu. 2015. Syntax-based deep matching of short texts. In Proceedings of the 24th International Joint Conference on Artificial Intelligence."},{"key":"e_1_3_2_48_2","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence","author":"Wang Shuohang","year":"2018","unstructured":"Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerry Tesauro, Bowen Zhou, and Jing Jiang. 2018. R 3: Reinforced ranker-reader for open-domain question answering. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence."},{"issue":"1","key":"e_1_3_2_49_2","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1145\/365153.365168","article-title":"ELIZA\u2013a computer program for the study of natural language communication between man and machine","volume":"9","author":"Weizenbaum Joseph","year":"1966","unstructured":"Joseph Weizenbaum. 1966. ELIZA\u2013a computer program for the study of natural language communication between man and machine. Communications of the ACM 9, 1 (1966), 36\u201345.","journal-title":"Communications of the ACM"},{"key":"e_1_3_2_50_2","volume-title":"Proceedings of the Interspeech 2020","author":"Whang Taesun","year":"2020","unstructured":"Taesun Whang, Dongyub Lee, Chanhee Lee, Kisu Yang, Dongsuk Oh, and HeuiSeok Lim. 2020. An effective domain adaptive post-training method for BERT in response selection. In Proceedings of the Interspeech 2020."},{"key":"e_1_3_2_51_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Wu Ledell","year":"2018","unstructured":"Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, and Jason Weston. 2018. Starspace: Embed all the things!. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_52_2","first-page":"496","volume-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics","author":"Wu Yu","year":"2017","unstructured":"Yu Wu, Wei Wu, Chen Xing, Ming Zhou, and Zhoujun Li. 2017. Sequential matching network: A new architecture for multi-turn response selection in retrieval-based chatbots. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Vancouver, Canada, 496\u2013505. DOI:10.18653\/v1\/P17-1046"},{"key":"e_1_3_2_53_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Wu Yu","year":"2018","unstructured":"Yu Wu, Wei Wu, Dejian Yang, Can Xu, and Zhoujun Li. 2018. Neural response generation with dynamic vocabularies. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_54_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Xing Chen","year":"2017","unstructured":"Chen Xing, Wei Wu, Yu Wu, Jie Liu, Yalou Huang, Ming Zhou, and Wei-Ying Ma. 2017. Topic aware neural response generation. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_55_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Xing Chen","year":"2018","unstructured":"Chen Xing, Yu Wu, Wei Wu, Yalou Huang, and Ming Zhou. 2018. Hierarchical recurrent attention network for response generation. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_56_2","volume-title":"Proceedings of the 35th AAAI Conference on Artificial Intelligence","author":"Xu Yi","year":"2021","unstructured":"Yi Xu, Hai Zhao, and Zhuosheng Zhang. 2021. Topicaware multi-turn dialogue modeling. In Proceedings of the 35th AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_57_2","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1145\/2911451.2911542","volume-title":"Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Yan Rui","year":"2016","unstructured":"Rui Yan, Yiping Song, and Hua Wu. 2016. Learning to respond with deep neural networks for retrieval-based human-computer conversation system. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. 55\u201364."},{"key":"e_1_3_2_58_2","first-page":"111","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing","author":"Yuan Chunyuan","year":"2019","unstructured":"Chunyuan Yuan, Wei Zhou, Mingming Li, Shangwen Lv, Fuqing Zhu, Jizhong Han, and Songlin Hu. 2019. Multi-hop selector network for multi-turn response selection in retrieval-based chatbots. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Association for Computational Linguistics, Hong Kong, China, 111\u2013120. DOI:10.18653\/v1\/D19-1011"},{"key":"e_1_3_2_59_2","doi-asserted-by":"crossref","first-page":"1990","DOI":"10.1145\/3442381.3449902","volume-title":"Proceedings of the Web Conference 2021","author":"Zhang Chen","year":"2021","unstructured":"Chen Zhang, Hao Wang, Feijun Jiang, and Hongzhi Yin. 2021. Adapting to context-aware knowledge in natural conversation for multi-turn response selection. In Proceedings of the Web Conference 2021. 1990\u20132001."},{"key":"e_1_3_2_60_2","first-page":"2204","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics","author":"Zhang Saizheng","year":"2018","unstructured":"Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018. 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. Association for Computational Linguistics, Melbourne, Australia, 2204\u20132213. DOI:10.18653\/v1\/P18-1205"},{"key":"e_1_3_2_61_2","first-page":"5443","volume-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence","author":"Zhao Xueliang","year":"2019","unstructured":"Xueliang Zhao, Chongyang Tao, Wei Wu, Can Xu, Dongyan Zhao, and Rui Yan. 2019. A document-grounded matching network for response selection in retrieval-based chatbots. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 5443\u20135449. DOI:10.24963\/ijcai.2019\/756"},{"key":"e_1_3_2_62_2","first-page":"3377","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing","author":"Zhao Xueliang","year":"2020","unstructured":"Xueliang Zhao, Wei Wu, Can Xu, Chongyang Tao, Dongyan Zhao, and Rui Yan. 2020. Knowledge-grounded dialogue generation with pre-trained language models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online, 3377\u20133390. DOI:10.18653\/v1\/2020.emnlp-main.272"},{"key":"e_1_3_2_63_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Zhou Hao","year":"2018","unstructured":"Hao Zhou, Minlie Huang, Tianyang Zhang, Xiaoyan Zhu, and Bing Liu. 2018. Emotional chatting machine: Emotional conversation generation with internal and external memory. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_64_2","doi-asserted-by":"crossref","first-page":"708","DOI":"10.18653\/v1\/D18-1076","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","author":"Zhou Kangyan","year":"2018","unstructured":"Kangyan Zhou, Shrimai Prabhumoye, and Alan W. Black. 2018. A dataset for document grounded conversations. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, Belgium, 708\u2013713. DOI:10.18653\/v1\/D18-1076"},{"key":"e_1_3_2_65_2","first-page":"1118","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics","author":"Zhou Xiangyang","year":"2018","unstructured":"Xiangyang Zhou, Lu Li, Daxiang Dong, Yi Liu, Ying Chen, Wayne Xin Zhao, Dianhai Yu, and Hua Wu. 2018. Multi-turn response selection for chatbots with deep attention matching network. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Melbourne, Australia, 1118\u20131127. 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