{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:02:18Z","timestamp":1774540938103,"version":"3.50.1"},"reference-count":66,"publisher":"Association for Computing Machinery (ACM)","issue":"3s","license":[{"start":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T00:00:00Z","timestamp":1677196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61727809, 62072423, 62006066"],"award-info":[{"award-number":["61727809, 62072423, 62006066"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Joint Funds of the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U22A2094"],"award-info":[{"award-number":["U22A2094"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"CAAI-Huawei MindSpore Open Fund","award":["CAAIXSJLJJ-2021-007B"],"award-info":[{"award-number":["CAAIXSJLJJ-2021-007B"]}]},{"name":"USTC Research Funds of the Double First-Class Initiative","award":["YD2150002009"],"award-info":[{"award-number":["YD2150002009"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"<jats:p>\n            As a vivid and linguistic symbol, Emojis have become a prevailing medium interspersed in text-based communication\u00a0(e.g., social media and chit-chat) to express emotions, attitudes, and situations. Generally speaking, a social-oriented chatbot that can generate appropriate Emoji-embedded responses would be much more competitive, making communications more fun, engaging, and human-like. However, the current Emoji-related research is still in its infancy, leading to an awkward situation of data deficiency. How to develop an Emoji-embedded dialogue system while addressing the lack of data will be interesting and meaningful for the application of future AI. To bridge this gap, we propose a multi-task learning method for persona-aware Emoji-embedded dialogue generation in this article. Specifically, as the benchmark of model training and evaluation, which includes 1.2 million Emoji-embedded tweets and 1.1 million post-response pairs, we first construct a dataset named\n            <jats:italic>EmojiTweet<\/jats:italic>\n            to handle the data deficiency problem. Then, a Seq2Seq-based model with multi-task learning is designed to simultaneously learn response generation and Emoji embedding from the constructed non-Emoji dialogue and Emoji-embedded monologue data. Afterward, we incorporate persona factors into our model by adopting persona fusion and personalized bias methods to deliver personalized dialogues with more accurately selected Emojis. Finally, we conduct extensive experiments, where the experimental results and evaluations demonstrate that our model has three key benefits: improved dialogue quality, higher user engagement, and not relying on large-scale Emoji-embedded dialogue data representing specific personas.\n            <jats:italic>EmojiTweet<\/jats:italic>\n            will be published publicly via\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/mea-lab-421.github.io\/EmojiTweet\/\">https:\/\/mea-lab-421.github.io\/EmojiTweet\/<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3571819","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T11:58:07Z","timestamp":1669118287000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["PEDM: A Multi-task Learning Model for Persona-aware Emoji-embedded Dialogue Generation"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8103-0321","authenticated-orcid":false,"given":"Sirui","family":"Zhao","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Southwest University of Science and Technology, Hefei, Mianyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1534-188X","authenticated-orcid":false,"given":"Hongyu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Thoughtworks, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5004-9756","authenticated-orcid":false,"given":"Hanqing","family":"Tao","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5735-3059","authenticated-orcid":false,"given":"Rui","family":"Zha","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0743-9003","authenticated-orcid":false,"given":"Kun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4246-5386","authenticated-orcid":false,"given":"Tong","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4835-4102","authenticated-orcid":false,"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,2,24]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"265","volume-title":"12th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201916)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, and Michael Isard. 2016. Tensorflow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201916). 265\u2013283."},{"key":"e_1_3_1_3_2","article-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau Dzmitry","year":"2014","unstructured":"Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).","journal-title":"arXiv preprint arXiv:1409.0473"},{"key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"4766","DOI":"10.18653\/v1\/D18-1508","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","author":"Barbieri Francesco","year":"2018","unstructured":"Francesco Barbieri, Luis Espinosa Anke, Jose Camacho-Collados, Steven Schockaert, and Horacio Saggion. 2018. Interpretable emoji prediction via label-wise attention LSTMs. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 4766\u20134771."},{"key":"e_1_3_1_5_2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.18653\/v1\/S18-1003","volume-title":"Proceedings of the 12th International Workshop on Semantic Evaluation","author":"Barbieri Francesco","year":"2018","unstructured":"Francesco Barbieri, Jose Camacho-Collados, Francesco Ronzano, Luis Espinosa Anke, Miguel Ballesteros, Valerio Basile, Viviana Patti, and Horacio Saggion. 2018. Semeval 2018 task 2: Multilingual emoji prediction. In Proceedings of the 12th International Workshop on Semantic Evaluation. 24\u201333."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1508"},{"key":"e_1_3_1_7_2","article-title":"Exploring emoji usage and prediction through a temporal variation lens","author":"Barbieri Francesco","year":"2018","unstructured":"Francesco Barbieri, Luis Marujo, Pradeep Karuturi, William Brendel, and Horacio Saggion. 2018. Exploring emoji usage and prediction through a temporal variation lens. arXiv preprint arXiv:1805.00731 (2018).","journal-title":"arXiv preprint arXiv:1805.00731"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.5555\/1622248.1622254"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944966"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","first-page":"4321","DOI":"10.1109\/BigData.2017.8258461","volume-title":"2017 IEEE International Conference on Big Data (Big Data\u201917)","author":"Berengueres Jose","year":"2017","unstructured":"Jose Berengueres and Dani Castro. 2017. Differences in emoji sentiment perception between readers and writers. In 2017 IEEE International Conference on Big Data (Big Data\u201917). IEEE, 4321\u20134328."},{"key":"e_1_3_1_11_2","unstructured":"S. H. Cappallo. 2018. Twemoji Dataset. Data retrieved from University of Amsterdam \/Amsterdam University of Applied Sciences. https:\/\/doi.org\/10.21942\/uva.5822100.v3"},{"key":"e_1_3_1_12_2","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014).","journal-title":"arXiv preprint arXiv:1412.3555"},{"key":"e_1_3_1_13_2","article-title":"Bam! Born-again multi-task networks for natural language understanding","author":"Clark Kevin","year":"2019","unstructured":"Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, Christopher D. Manning, and Quoc V. Le. 2019. Bam! Born-again multi-task networks for natural language understanding. arXiv preprint arXiv:1907.04829 (2019).","journal-title":"arXiv preprint arXiv:1907.04829"},{"key":"e_1_3_1_14_2","volume-title":"CogSci","author":"Cohn Neil","year":"2018","unstructured":"Neil Cohn, Tim Roijackers, Robin Schaap, and Jan Engelen. 2018. Are emoji a poor substitute for words? Sentence processing with emoji substitutions. In CogSci."},{"key":"e_1_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Jan Deriu Alvaro Rodrigo Arantxa Otegi Guillermo Echegoyen Sophie Rosset Eneko Agirre and Mark Cieliebak. 2021. Survey on evaluation methods for dialogue systems. Artificial Intelligence Review 54 1 (2021) 755\u2013810.","DOI":"10.1007\/s10462-020-09866-x"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210183"},{"key":"e_1_3_1_17_2","first-page":"1","article-title":"Are GRU cells more specific and LSTM cells more sensitive in motive classification of text","volume":"3","author":"Gruber Nicole","year":"2020","unstructured":"Nicole Gruber and Alfred Jockisch. 2020. Are GRU cells more specific and LSTM cells more sensitive in motive classification of text. J. Front. Artif. Intell 3 (2020), 1\u20136.","journal-title":"J. Front. Artif. Intell"},{"issue":"3","key":"e_1_3_1_18_2","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1080\/17544750.2016.1213757","article-title":"WeChat: Social and political development of China\u2019s dominant messaging app","volume":"10","author":"Harwit Eric","year":"2017","unstructured":"Eric Harwit. 2017. WeChat: Social and political development of China\u2019s dominant messaging app. Chinese Journal of Communication 10, 3 (2017), 312\u2013327.","journal-title":"Chinese Journal of Communication"},{"key":"e_1_3_1_19_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.18653\/v1\/W19-1311","volume-title":"Proceedings of the 10th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis","author":"Hayati Shirley Anugrah","year":"2019","unstructured":"Shirley Anugrah Hayati and Aldrian Obaja Muis. 2019. Analyzing incorporation of emotion in emoji prediction. In Proceedings of the 10th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 91\u201399."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413679"},{"key":"e_1_3_1_21_2","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/978-3-540-85483-8_14","volume-title":"International Workshop on Intelligent Virtual Agents","author":"Hernault Hugo","year":"2008","unstructured":"Hugo Hernault, Paul Piwek, Helmut Prendinger, and Mitsuru Ishizuka. 2008. Generating dialogues for virtual agents using nested textual coherence relations. In International Workshop on Intelligent Virtual Agents. Springer, 139\u2013145."},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"issue":"3","key":"e_1_3_1_23_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3383123","article-title":"Challenges in building intelligent open-domain dialog systems","volume":"38","author":"Huang Minlie","year":"2020","unstructured":"Minlie Huang, Xiaoyan Zhu, and Jianfeng Gao. 2020. Challenges in building intelligent open-domain dialog systems. ACM Transactions on Information Systems (TOIS) 38, 3 (2020), 1\u201332.","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"e_1_3_1_24_2","first-page":"505","volume-title":"IEEE International Conference on Cyber Physical and Social Computing (CPSCom\u201920)","author":"Jiang Hongyu","year":"2020","unstructured":"Hongyu Jiang, Ao Guo, and Jianhua Ma. 2020. Automatic prediction and insertion of multiple emojis in social media text. In IEEE International Conference on Cyber Physical and Social Computing (CPSCom\u201920). IEEE, 505\u2013512."},{"key":"e_1_3_1_25_2","first-page":"296","volume-title":"International Conference on Cyber Science and Technology Congress (CyberSciTech\u201920)","author":"Jiang Hongyu","year":"2020","unstructured":"Hongyu Jiang, Ao Guo, and Jianhua Ma. 2020. Genre-based emoji usage analysis and presiction in video comments. In International Conference on Cyber Science and Technology Congress (CyberSciTech\u201920). IEEE, 296\u2013305."},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2020.106648"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2016.02.088"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3373146"},{"key":"e_1_3_1_29_2","first-page":"3728","volume-title":"IJCAI","author":"Kottur Satwik","year":"2017","unstructured":"Satwik Kottur, Xiaoyu Wang, and V\u00edtor Carvalho. 2017. Exploring personalized neural conversational models. In IJCAI. 3728\u20133734."},{"key":"e_1_3_1_30_2","article-title":"A diversity-promoting objective function for neural conversation models","author":"Li Jiwei","year":"2015","unstructured":"Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2015. A diversity-promoting objective function for neural conversation models. arXiv preprint arXiv:1510.03055 (2015).","journal-title":"arXiv preprint arXiv:1510.03055"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1014"},{"key":"e_1_3_1_32_2","article-title":"A persona-based neural conversation model","author":"Li Jiwei","year":"2016","unstructured":"Jiwei Li, Michel Galley, Chris Brockett, Georgios P. Spithourakis, Jianfeng Gao, and Bill Dolan. 2016. A persona-based neural conversation model. arXiv preprint arXiv:1603.06155 (2016).","journal-title":"arXiv preprint arXiv:1603.06155"},{"key":"e_1_3_1_33_2","volume-title":"12th International AAAI Conference on Web and Social Media","author":"Li Weijian","year":"2018","unstructured":"Weijian Li, Yuxiao Chen, Tianran Hu, and Jiebo Luo. 2018. Mining the relationship between emoji usage patterns and personality. In 12th International AAAI Conference on Web and Social Media."},{"key":"e_1_3_1_34_2","first-page":"1","volume-title":"2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA\u201914)","author":"Liang Weibin","year":"2014","unstructured":"Weibin Liang, Hsienchang Wang, Yian Chu, and Chunghsien Wu. 2014. Emoticon recommendation in microblog using affective trajectory model. In 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA\u201914). IEEE, 1\u20135."},{"key":"e_1_3_1_35_2","article-title":"Multi-task learning for speaker-role adaptation in neural conversation models","author":"Luan Yi","year":"2017","unstructured":"Yi Luan, Chris Brockett, Bill Dolan, Jianfeng Gao, and Michel Galley. 2017. Multi-task learning for speaker-role adaptation in neural conversation models. arXiv preprint arXiv:1710.07388 (2017).","journal-title":"arXiv preprint arXiv:1710.07388"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2019.2900910"},{"key":"e_1_3_1_37_2","volume-title":"Proceedings of the 1st International Workshop on Emoji Understanding and Applications in Social Media, Standford [en l\u00ednea].","author":"McCulloch Gretchen","year":"2018","unstructured":"Gretchen McCulloch and Lauren Gawne. 2018. Emoji grammar as beat gestures. In Proceedings of the 1st International Workshop on Emoji Understanding and Applications in Social Media, Standford [en l\u00ednea]. Disponible en http:\/\/knoesis.org\/resources\/Emoji2018\/Emoji2018_Papers\/Paper13_Emoji2018.pdf [Consulta 11\/12\/2019]."},{"issue":"10","key":"e_1_3_1_38_2","first-page":"2659","article-title":"Food recommendation: Framework, existing solutions, and challenges","volume":"22","author":"Min Weiqing","year":"2019","unstructured":"Weiqing Min, Shuqiang Jiang, and Ramesh Jain. 2019. Food recommendation: Framework, existing solutions, and challenges. IEEE Transactions on Multimedia 22, 10 (2019), 2659\u20132671.","journal-title":"IEEE Transactions on Multimedia"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.3115\/1075096.1075117"},{"key":"e_1_3_1_40_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. 311\u2013318."},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186005"},{"key":"e_1_3_1_42_2","first-page":"4279","volume-title":"IJCAI","author":"Qian Qiao","year":"2018","unstructured":"Qiao Qian, Minlie Huang, Haizhou Zhao, Jingfang Xu, and Xiaoyan Zhu. 2018. Assigning personality\/profile to a chatting machine for coherent conversation generation. In IJCAI. 4279\u20134285."},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3551604"},{"key":"e_1_3_1_44_2","article-title":"An overview of multi-task learning in deep neural networks","author":"Ruder Sebastian","year":"2017","unstructured":"Sebastian Ruder. 2017. An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017).","journal-title":"arXiv preprint arXiv:1706.05098"},{"key":"e_1_3_1_45_2","doi-asserted-by":"crossref","DOI":"10.1017\/9781108677387","volume-title":"The Emoji Revolution: How Technology is Shaping the Future of Communication","author":"Seargeant Philip","year":"2019","unstructured":"Philip Seargeant. 2019. The Emoji Revolution: How Technology is Shaping the Future of Communication. Cambridge University Press."},{"key":"e_1_3_1_46_2","article-title":"Improving neural machine translation models with monolingual data","author":"Sennrich Rico","year":"2015","unstructured":"Rico Sennrich, Barry Haddow, and Alexandra Birch. 2015. Improving neural machine translation models with monolingual data. Computer Ence (2015).","journal-title":"Computer Ence"},{"key":"e_1_3_1_47_2","unstructured":"Iulian Vlad Serban Ryan Lowe Peter Henderson Laurent Charlin and Joelle Pineau. 2015. A survey of available corpora for building data-driven dialogue systems. arXiv preprint arXiv:1512.05742 (2015)."},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.17706\/IJCEE.2017.9.1.360-369"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1631\/FITEE.1700826"},{"key":"e_1_3_1_50_2","article-title":"Exploiting persona information for diverse generation of conversational responses","author":"Song Haoyu","year":"2019","unstructured":"Haoyu Song, Wei-Nan Zhang, Yiming Cui, Dong Wang, and Ting Liu. 2019. Exploiting persona information for diverse generation of conversational responses. arXiv preprint arXiv:1905.12188 (2019).","journal-title":"arXiv preprint arXiv:1905.12188"},{"key":"e_1_3_1_51_2","article-title":"A neural network approach to context-sensitive generation of conversational responses","author":"Sordoni Alessandro","year":"2015","unstructured":"Alessandro Sordoni, Michel Galley, Michael Auli, Chris Brockett, Yangfeng Ji, Margaret Mitchell, Jian-Yun Nie, Jianfeng Gao, and Bill Dolan. 2015. A neural network approach to context-sensitive generation of conversational responses. arXiv preprint arXiv:1506.06714 (2015).","journal-title":"arXiv preprint arXiv:1506.06714"},{"key":"e_1_3_1_52_2","first-page":"3104","volume-title":"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 Advances in Neural Information Processing Systems. 3104\u20133112."},{"key":"e_1_3_1_53_2","unstructured":"Marilyn Walker Grace Lin and Jennifer Sawyer. 2012. An annotated corpus of film dialogue for learning and characterizing character style. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC\u201912) . 1373\u20131378."},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2016.03.040"},{"key":"e_1_3_1_55_2","first-page":"1","volume-title":"Proceedings of the 9th SIGHAN Workshop on Chinese Language Processing","author":"Wang Jianan","year":"2017","unstructured":"Jianan Wang, Xin Wang, Fang Li, Zhen Xu, Zhuoran Wang, and Baoxun Wang. 2017. Group linguistic bias aware neural response generation. In Proceedings of the 9th SIGHAN Workshop on Chinese Language Processing. 1\u201310."},{"key":"e_1_3_1_56_2","article-title":"Sequence-to-sequence learning for task-oriented dialogue with dialogue state representation","author":"Wen Haoyang","year":"2018","unstructured":"Haoyang Wen, Yijia Liu, Wanxiang Che, Libo Qin, and Ting Liu. 2018. Sequence-to-sequence learning for task-oriented dialogue with dialogue state representation. arXiv preprint arXiv:1806.04441 (2018).","journal-title":"arXiv preprint arXiv:1806.04441"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3267305.3274181"},{"key":"e_1_3_1_58_2","article-title":"Tweet emoji prediction using hierarchical model with attention","author":"Wu Chuhan","year":"2018","unstructured":"Chuhan Wu, Fangzhao Wu, Sixing Wu, Yongfeng Huang, and Xing Xie. 2018. Tweet emoji prediction using hierarchical model with attention. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers.","journal-title":"Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376416"},{"key":"e_1_3_1_60_2","article-title":"Attribute2Image: Conditional image generation from visual attributes","volume":"1512","author":"Yan Xinchen","year":"2015","unstructured":"Xinchen Yan, Jimei Yang, Kihyuk Sohn, and Honglak Lee. 2015. Attribute2Image: Conditional image generation from visual attributes. CoRR abs\/1512.00570 (2015). arXiv:1512.00570http:\/\/arxiv.org\/abs\/1512.00570.","journal-title":"CoRR"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1205"},{"key":"e_1_3_1_62_2","article-title":"A survey on multi-task learning","author":"Zhang Yu","year":"2021","unstructured":"Yu Zhang and Qiang Yang. 2021. A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering (2021).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_63_2","unstructured":"Tiancheng Zhao Ran Zhao and Maxine Esk\u00e9nazi. 2017. Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. ArXiv abs\/1703.10960 (2017)."},{"key":"e_1_3_1_64_2","article-title":"Personalized dialogue generation with diversified traits","author":"Zheng Yinhe","year":"2019","unstructured":"Yinhe Zheng, Guanyi Chen, Minlie Huang, Song Liu, and Xuan Zhu. 2019. Personalized dialogue generation with diversified traits. arXiv preprint arXiv:1901.09672 (2019).","journal-title":"arXiv preprint arXiv:1901.09672"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2020.3003864"},{"key":"e_1_3_1_66_2","article-title":"The design and implementation of XiaoIce, an empathetic social chatbot","author":"Zhou Li","year":"2018","unstructured":"Li Zhou, Jianfeng Gao, Di Li, and Heung-Yeung Shum. 2018. The design and implementation of XiaoIce, an empathetic social chatbot. arXiv preprint arXiv:1812.08989 (2018).","journal-title":"arXiv preprint arXiv:1812.08989"},{"key":"e_1_3_1_67_2","article-title":"Multi-task learning with language modeling for question generation","author":"Zhou Wenjie","year":"2019","unstructured":"Wenjie Zhou, Minghua Zhang, and Yunfang Wu. 2019. Multi-task learning with language modeling for question generation. arXiv preprint arXiv:1908.11813 (2019).","journal-title":"arXiv preprint arXiv:1908.11813"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3571819","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3571819","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:48:48Z","timestamp":1750182528000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3571819"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,24]]},"references-count":66,"journal-issue":{"issue":"3s","published-print":{"date-parts":[[2023,10,31]]}},"alternative-id":["10.1145\/3571819"],"URL":"https:\/\/doi.org\/10.1145\/3571819","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,24]]},"assertion":[{"value":"2021-07-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-11-11","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}