{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:45:11Z","timestamp":1787017511662,"version":"build-2736575974"},"reference-count":68,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T00:00:00Z","timestamp":1705881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>\n                    Recent progress in deep learning has continuously improved the accuracy of dialogue response selection. However, in real-world scenarios, the high computation cost forces existing dialogue response selection models to rank only a small number of candidates, recalled by a coarse-grained model, precluding many high-quality candidates. To overcome this problem, we present a novel and efficient response selection model and a set of tailor-designed learning strategies to train it effectively. The proposed model consists of a dense retrieval module and an interaction layer, which could directly select the proper response from a large corpus. We conduct re-rank and full-rank evaluations on widely used benchmarks to evaluate our proposed model. Extensive experimental results demonstrate that our proposed model notably outperforms the state-of-the-art baselines on both re-rank and full-rank evaluations. Moreover, human evaluation results show that the response quality could be improved further by enlarging the candidate pool with nonparallel corpora. In addition, we also release high-quality benchmarks that are carefully annotated for more accurate dialogue response selection evaluation. All source codes, datasets, model parameters, and other related resources have been publicly available.\n                    <jats:xref ref-type=\"fn\">\n                      <jats:sup>1<\/jats:sup>\n                    <\/jats:xref>\n                  <\/jats:p>","DOI":"10.1145\/3632750","type":"journal-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T06:33:49Z","timestamp":1699943629000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Exploring Dense Retrieval for Dialogue Response Selection"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5200-1537","authenticated-orcid":false,"given":"Tian","family":"Lan","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3444-8383","authenticated-orcid":false,"given":"Deng","family":"Cai","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0322-8479","authenticated-orcid":false,"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"Independent Researcher, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1472-7791","authenticated-orcid":false,"given":"Yixuan","family":"Su","sequence":"additional","affiliation":[{"name":"Language Technology Lab, University of Cambridge, England"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0320-7520","authenticated-orcid":false,"given":"Heyan","family":"Huang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6795-2311","authenticated-orcid":false,"given":"Xian-Ling","family":"Mao","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,1,22]]},"reference":[{"key":"e_1_3_3_2_2","article-title":"Towards a human-like open-domain chatbot","volume":"2001","author":"Adiwardana D.","year":"2020","unstructured":"D. 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. ArXiv abs\/2001.09977 (2020).","journal-title":"ArXiv"},{"key":"e_1_3_3_3_2","volume-title":"ACL","author":"Bao Siqi","year":"2020","unstructured":"Siqi Bao, H. He, Fan Wang, and Hua Wu. 2020. PLATO: Pre-trained dialogue generation model with discrete latent variable. In ACL."},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682538"},{"key":"e_1_3_3_5_2","volume-title":"ACL","author":"Chen Wei","year":"2022","unstructured":"Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiao-Mei Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, and Nan Duan. 2022. Contextual fine-to-coarse distillation for coarse-grained response selection in open-domain conversations. In ACL."},{"key":"e_1_3_3_6_2","unstructured":"David R. Cheriton. 2019. From doc2query to docTTTTTquery."},{"key":"e_1_3_3_7_2","article-title":"MuTual: A dataset for multi-turn dialogue reasoning","volume":"2004","author":"Cui Leyang","year":"2020","unstructured":"Leyang Cui, Yu Wu, Shujie Liu, Yue Zhang, and Ming Zhou. 2020. MuTual: A dataset for multi-turn dialogue reasoning. ArXiv abs\/2004.04494 (2020).","journal-title":"ArXiv"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1037\/h0031619"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403211"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403211"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W19-2310"},{"key":"e_1_3_3_12_2","article-title":"Speaker-aware BERT for multi-turn response selection in retrieval-based chatbots","author":"Gu Jia-Chen","year":"2020","unstructured":"Jia-Chen Gu, Tianda Li, Quan Liu, Xiao-Dan Zhu, Zhenhua Ling, Zhiming Su, and Si Wei. 2020. Speaker-aware BERT for multi-turn response selection in retrieval-based chatbots. Proceedings of the 29th ACM International Conference on Information & Knowledge Management (2020).","journal-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358140"},{"key":"e_1_3_3_14_2","article-title":"Partner matters! An empirical study on fusing personas for personalized response selection in retrieval-based chatbots","author":"Gu Jia-Chen","year":"2021","unstructured":"Jia-Chen Gu, Hui Liu, Zhenhua Ling, Quan Liu, Zhigang Chen, and Xiaodan Zhu. 2021. Partner matters! An empirical study on fusing personas for personalized response selection in retrieval-based chatbots. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (2021).","journal-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.122"},{"key":"e_1_3_3_16_2","article-title":"GRADE: Automatic graph-enhanced coherence metric for evaluating open-domain dialogue systems","author":"Huang Lishan","year":"2020","unstructured":"Lishan Huang, Zheng Ye, Jinghui Qin, Liang Lin, and Xiaodan Liang. 2020. GRADE: Automatic graph-enhanced coherence metric for evaluating open-domain dialogue systems. arXiv preprint arXiv:2010.03994 (2020).","journal-title":"arXiv preprint arXiv:2010.03994"},{"key":"e_1_3_3_17_2","volume-title":"ICLR","author":"Humeau Samuel","year":"2020","unstructured":"Samuel Humeau, Kurt Shuster, Marie-Anne Lachaux, and J. Weston. 2020. Poly-encoders: Architectures and pre-training strategies for fast and accurate multi-sentence scoring. In ICLR."},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/582415.582418"},{"key":"e_1_3_3_19_2","article-title":"Billion-scale similarity search with GPUs","author":"Johnson Jeff","year":"2017","unstructured":"Jeff Johnson, Matthijs Douze, and Herv\u00e9 J\u00e9gou. 2017. Billion-scale similarity search with GPUs. arXiv preprint arXiv:1702.08734 (2017).","journal-title":"arXiv preprint arXiv:1702.08734"},{"key":"e_1_3_3_20_2","article-title":"Dense passage retrieval for open-domain question answering","volume":"2004","author":"Karpukhin Vladimir","year":"2020","unstructured":"Vladimir Karpukhin, Barlas Ouz, Sewon Min, Patrick Lewis, Ledell Yu Wu, Sergey Edunov, Danqi Chen, and Wen tau Yih. 2020. Dense passage retrieval for open-domain question answering. ArXiv abs\/2004.04906 (2020).","journal-title":"ArXiv"},{"key":"e_1_3_3_21_2","article-title":"ColBERT: Efficient and effective passage search via contextualized late interaction over BERT","author":"Khattab O.","year":"2020","unstructured":"O. Khattab and Matei A. Zaharia. 2020. ColBERT: Efficient and effective passage search via contextualized late interaction over BERT. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020).","journal-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval"},{"key":"e_1_3_3_22_2","article-title":"Adam: A method for stochastic optimization","volume":"1412","author":"Kingma Diederik P.","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. CoRR abs\/1412.6980 (2015).","journal-title":"CoRR"},{"issue":"1","key":"e_1_3_3_23_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3423168","article-title":"PONE: A novel automatic evaluation metric for open-domain generative dialogue systems","volume":"39","author":"Lan Tian","year":"2020","unstructured":"Tian Lan, Xian-Ling Mao, Wei Wei, Xiaoyan Gao, and Heyan Huang. 2020. PONE: A novel automatic evaluation metric for open-domain generative dialogue systems. ACM Transactions on Information Systems (TOIS) 39, 1 (2020), 1\u201337.","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"e_1_3_3_24_2","volume-title":"EMNLP","author":"Li Jiwei","year":"2016","unstructured":"Jiwei Li, Will Monroe, Alan Ritter, Dan Jurafsky, Michel Galley, and Jianfeng Gao. 2016. Deep reinforcement learning for dialogue generation. In EMNLP."},{"key":"e_1_3_3_25_2","article-title":"The world is not binary: Learning to rank with grayscale data for dialogue response selection","author":"Lin Zibo","year":"2020","unstructured":"Zibo Lin, Deng Cai, Yan Wang, Xiaojiang Liu, Hai-Tao Zheng, and Shuming Shi. 2020. The world is not binary: Learning to rank with grayscale data for dialogue response selection. arXiv preprint arXiv:2004.02421 (2020).","journal-title":"arXiv preprint arXiv:2004.02421"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i15.17582"},{"key":"e_1_3_3_27_2","article-title":"GPTEval: NLG evaluation using GPT-4 with better human alignment","author":"Liu Yang","year":"2023","unstructured":"Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023. GPTEval: NLG evaluation using GPT-4 with better human alignment. arXiv preprint arXiv:2303.16634 (2023).","journal-title":"arXiv preprint arXiv:2303.16634"},{"key":"e_1_3_3_28_2","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","volume":"1907","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, M. Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A robustly optimized BERT pretraining approach. ArXiv abs\/1907.11692 (2019).","journal-title":"ArXiv"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W15-4640"},{"key":"e_1_3_3_30_2","article-title":"Unsupervised evaluation of interactive dialog with dialoGPT","author":"Mehri Shikib","year":"2020","unstructured":"Shikib Mehri and Maxine Eskenazi. 2020. Unsupervised evaluation of interactive dialog with dialoGPT. arXiv preprint arXiv:2006.12719 (2020).","journal-title":"arXiv preprint arXiv:2006.12719"},{"key":"e_1_3_3_31_2","doi-asserted-by":"crossref","first-page":"3108","DOI":"10.1109\/CVPR.2012.6248043","article-title":"Fast search in Hamming space with multi-index hashing","author":"Norouzi Mohammad","year":"2012","unstructured":"Mohammad Norouzi, Ali Punjani, and David J. Fleet. 2012. Fast search in Hamming space with multi-index hashing. 2012 IEEE Conference on Computer Vision and Pattern Recognition (2012), 3108\u20133115.","journal-title":"2012 IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.279"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1191"},{"key":"e_1_3_3_34_2","article-title":"SetRank: Learning a permutation-invariant ranking model for information retrieval","author":"Pang Liang","year":"2020","unstructured":"Liang Pang, Jun Xu, Qingyao Ai, Yanyan Lan, Xueqi Cheng, and Jirong Wen. 2020. SetRank: Learning a permutation-invariant ranking model for information retrieval. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (2020).","journal-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.3115\/1073083.1073135"},{"key":"e_1_3_3_36_2","volume-title":"NeurIPS","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, S. Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, N. Gimelshein, L. Antiga, Alban Desmaison, Andreas K\u00f6pf, E. Yang, Zach 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 NeurIPS."},{"key":"e_1_3_3_37_2","article-title":"RocketQA: An optimized training approach to dense passage retrieval for open-domain question answering","volume":"2010","author":"Qu Yingqi","year":"2021","unstructured":"Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. 2021. RocketQA: An optimized training approach to dense passage retrieval for open-domain question answering. ArXiv abs\/2010.08191 (2021).","journal-title":"ArXiv"},{"key":"e_1_3_3_38_2","article-title":"RocketQAv2: A joint training method for dense passage retrieval and passage re-ranking","volume":"2110","author":"Ren Ruiyang","year":"2021","unstructured":"Ruiyang Ren, Yingqi Qu, Jing Liu, Wayne Xin Zhao, Qiaoqiao She, Hua Wu, Haifeng Wang, and Ji-Rong Wen. 2021. RocketQAv2: A joint training method for dense passage retrieval and passage re-ranking. ArXiv abs\/2110.07367 (2021).","journal-title":"ArXiv"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1561\/1500000019"},{"key":"e_1_3_3_40_2","volume-title":"EACL","author":"Roller Stephen","year":"2021","unstructured":"Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric Michael Smith, Y.-Lan Boureau, and Jason Weston. 2021. Recipes for building an open-domain chatbot. In EACL."},{"key":"e_1_3_3_41_2","volume-title":"Proceedings of Workshop on Text Summarization of ACL, Spain","author":"ROUGE C. Y. Lin.","year":"2004","unstructured":"C. Y. Lin. ROUGE. 2004. A package for automatic evaluation of summaries. In Proceedings of Workshop on Text Summarization of ACL, Spain."},{"key":"e_1_3_3_42_2","article-title":"ColBERTv2: Effective and efficient retrieval via lightweight late interaction","volume":"2112","author":"Santhanam Keshav","year":"2021","unstructured":"Keshav Santhanam, O. Khattab, Jon Saad-Falcon, Christopher Potts, and Matei A. Zaharia. 2021. ColBERTv2: Effective and efficient retrieval via lightweight late interaction. ArXiv abs\/2112.01488 (2021).","journal-title":"ArXiv"},{"key":"e_1_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2003.1238663"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.137"},{"key":"e_1_3_3_45_2","article-title":"TaCL: Improving BERT pre-training with token-aware contrastive learning","volume":"2111","author":"Su Yixuan","year":"2021","unstructured":"Yixuan Su, Fangyu Liu, Zaiqiao Meng, Tian Lan, Lei Shu, Ehsan Shareghi, and Nigel Collier. 2021. TaCL: Improving BERT pre-training with token-aware contrastive learning. CoRR abs\/2111.04198 (2021). arXiv:2111.04198https:\/\/arxiv.org\/abs\/2111.04198","journal-title":"CoRR"},{"key":"e_1_3_3_46_2","volume-title":"IJCAI","author":"Tao Chongyang","year":"2021","unstructured":"Chongyang Tao, Jiazhan Feng, Rui Yan, Wei Wu, and Daxin Jiang. 2021. A survey on response selection for retrieval-based dialogues. In IJCAI."},{"key":"e_1_3_3_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290985"},{"key":"e_1_3_3_48_2","volume-title":"ACL","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 ACL."},{"key":"e_1_3_3_49_2","article-title":"Attention is all you need","volume":"1706","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. ArXiv abs\/1706.03762 (2017).","journal-title":"ArXiv"},{"key":"e_1_3_3_50_2","doi-asserted-by":"crossref","unstructured":"Jiaan Wang Yunlong Liang Fandong Meng Zengkui Sun Haoxiang Shi Zhixu Li Jinan Xu Jianfeng Qu and Jie Zhou. 2023. Is ChatGPT a Good NLG Evaluator? A Preliminary Study. arxiv:2303.04048 [cs.CL]","DOI":"10.18653\/v1\/2023.newsum-1.1"},{"key":"e_1_3_3_51_2","volume-title":"INTERSPEECH","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 INTERSPEECH."},{"key":"e_1_3_3_52_2","volume-title":"AAAI","author":"Whang Taesun","year":"2021","unstructured":"Taesun Whang, Dongyub Lee, Dongsuk Oh, Chanhee Lee, Kijong Han, Donghun Lee, and Saebyeok Lee. 2021. Do response selection models really know what\u2019s next? Utterance manipulation strategies for multi-turn response selection. In AAAI."},{"key":"e_1_3_3_53_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"e_1_3_3_54_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1046"},{"key":"e_1_3_3_55_2","volume-title":"AAAI","author":"Xu Ruijian","year":"2021","unstructured":"Ruijian Xu, Chongyang Tao, Daxin Jiang, Xueliang Zhao, Dongyan Zhao, and Rui Yan. 2021. Learning an effective context-response matching model with self-supervised tasks for retrieval-based dialogues. In AAAI."},{"key":"e_1_3_3_56_2","article-title":"Incorporating loose-structured knowledge into LSTM with recall gate for conversation modeling","volume":"1605","author":"Xu Zhen","year":"2016","unstructured":"Zhen Xu, B. Liu, Baoxun Wang, Chengjie Sun, and Xiaolong Wang. 2016. Incorporating loose-structured knowledge into LSTM with recall gate for conversation modeling. ArXiv abs\/1605.05110 (2016).","journal-title":"ArXiv"},{"key":"e_1_3_3_57_2","article-title":"Efficient passage retrieval with hashing for open-domain question answering","volume":"2106","author":"Yamada Ikuya","year":"2021","unstructured":"Ikuya Yamada, Akari Asai, and Hannaneh Hajishirzi. 2021. Efficient passage retrieval with hashing for open-domain question answering. ArXiv abs\/2106.00882 (2021). https:\/\/api.semanticscholar.org\/CorpusID:235293983","journal-title":"ArXiv"},{"key":"e_1_3_3_58_2","article-title":"A comprehensive assessment of dialog evaluation metrics","author":"Yeh Yi-Ting","year":"2021","unstructured":"Yi-Ting Yeh, Maxine Eskenazi, and Shikib Mehri. 2021. A comprehensive assessment of dialog evaluation metrics. arXiv preprint arXiv:2106.03706 (2021).","journal-title":"arXiv preprint arXiv:2106.03706"},{"key":"e_1_3_3_59_2","article-title":"Few-shot conversational dense retrieval","author":"Yu Shih Yuan","year":"2021","unstructured":"Shih Yuan Yu, Zhenghao Liu, Chenyan Xiong, Tao Feng, and Zhiyuan Liu. 2021. Few-shot conversational dense retrieval. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (2021). https:\/\/api.semanticscholar.org\/CorpusID:234343311","journal-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval"},{"key":"e_1_3_3_60_2","volume-title":"EMNLP\/IJCNLP","author":"Yuan Chunyuan","year":"2019","unstructured":"Chunyuan Yuan, W. 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 EMNLP\/IJCNLP."},{"key":"e_1_3_3_61_2","article-title":"DynaEval: Unifying turn and dialogue level evaluation","author":"Zhang Chen","year":"2021","unstructured":"Chen Zhang, Yiming Chen, Luis Fernando D\u2019Haro, Yan Zhang, Thomas Friedrichs, Grandee Lee, and Haizhou Li. 2021. DynaEval: Unifying turn and dialogue level evaluation. arXiv preprint arXiv:2106.01112 (2021).","journal-title":"arXiv preprint arXiv:2106.01112"},{"key":"e_1_3_3_62_2","article-title":"BERTScore: Evaluating text generation with BERT","author":"Zhang Tianyi","year":"2019","unstructured":"Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2019. BERTScore: Evaluating text generation with BERT. arXiv preprint arXiv:1904.09675 (2019).","journal-title":"arXiv preprint arXiv:1904.09675"},{"key":"e_1_3_3_63_2","unstructured":"Xiao Zhang Heyan Huang Zewen Chi and Xian-Ling Mao. 2022. ET5: A Novel End-to-End Framework for Conversational Machine Reading Comprehension. arxiv:2209.11484 [cs.CL]"},{"key":"e_1_3_3_64_2","first-page":"3740","volume-title":"Proceedings of the 27th International Conference on Computational Linguistics","author":"Zhang Zhuosheng","year":"2018","unstructured":"Zhuosheng Zhang, Jiangtong Li, Pengfei Zhu, Hai Zhao, and Gongshen Liu. 2018. Modeling multi-turn conversation with deep utterance aggregation. In Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics, Santa Fe, New Mexico, USA, 3740\u20133752. https:\/\/aclanthology.org\/C18-1317"},{"key":"e_1_3_3_65_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.299"},{"key":"e_1_3_3_66_2","doi-asserted-by":"crossref","unstructured":"Sizhe Zhou Siru Ouyang Zhuosheng Zhang and Hai Zhao. 2022. Towards End-to-End Open Conversational Machine Reading. arxiv:2210.07113 [cs.CL]","DOI":"10.18653\/v1\/2023.findings-eacl.154"},{"key":"e_1_3_3_67_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6521"},{"key":"e_1_3_3_68_2","volume-title":"EMNLP","author":"Zhou Xiangyang","year":"2016","unstructured":"Xiangyang Zhou, Daxiang Dong, Hua Wu, Shiqi Zhao, Dianhai Yu, Hao Tian, Xuan Liu, and Rui Yan. 2016. Multi-view response selection for human-computer conversation. In EMNLP."},{"key":"e_1_3_3_69_2","volume-title":"ACL","author":"Zhou Xiangyang","year":"2018","unstructured":"Xiangyang Zhou, Lu Li, Daxiang Dong, Y. Liu, Ying Chen, Wayne Xin Zhao, Dianhai Yu, and Hua Wu. 2018. Multi-turn response selection for chatbots with deep attention matching network. In ACL."}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3632750","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3632750","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T18:51:04Z","timestamp":1750272664000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3632750"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,22]]},"references-count":68,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,5,31]]}},"alternative-id":["10.1145\/3632750"],"URL":"https:\/\/doi.org\/10.1145\/3632750","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,22]]},"assertion":[{"value":"2022-11-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-06","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-01-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}