{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T05:24:55Z","timestamp":1730265895194,"version":"3.28.0"},"reference-count":25,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1109\/ijcnn.2019.8852308","type":"proceedings-article","created":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T03:44:32Z","timestamp":1569901472000},"page":"1-8","source":"Crossref","is-referenced-by-count":0,"title":["MIDS: End-to-End Personalized Response Generation in Untrimmed Multi-Role Dialogue"],"prefix":"10.1109","author":[{"given":"Qichuan","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Beihang University, Beijing, China"}]},{"given":"Zhiqiang","family":"He","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Beihang University, Lenovo Ltd., Beijing, China"}]},{"given":"Zhiqiang","family":"Zhan","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Jianyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"AI Lab of Research and Development Lenovo Ltd., Beijing, China"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Research and Development Lenovo Ltd., Beijing, China"}]},{"given":"Changjian","family":"Hu","sequence":"additional","affiliation":[{"name":"AI Lab of Research and Development Lenovo Ltd., Beijing, China"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7965952"},{"key":"ref11","article-title":"Learning to update auto-associative memory in recurrent neural networks for improving sequence memorization","author":"zhang","year":"2017","journal-title":"Computing Research Repository"},{"key":"ref12","article-title":"Improving frame semantic parsing with hierarchical dialogue encoders","author":"bapna","year":"2017","journal-title":"Computing Research Repository"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7965952"},{"key":"ref14","article-title":"Affective neural response generation","author":"asghar","year":"2017","journal-title":"European Conference on Information Retrieval"},{"key":"ref15","article-title":"Cold fusion: training seq2seq models together with language models","author":"sriram","year":"2017","journal-title":"Computing Research Repository"},{"key":"ref16","article-title":"Convolutional sequence to sequence learning","author":"gehring","year":"2017","journal-title":"Computing Research Repository"},{"key":"ref17","article-title":"Abduc-tive matching in question answering","author":"dhamdhere","year":"2017","journal-title":"Computing Research Repository"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1228"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-5528"},{"key":"ref4","first-page":"163","article-title":"Speaker role contextual modeling for language understanding and dialogue policy learning","author":"chi","year":"2017","journal-title":"International Joint Conference on Natural Language Processing"},{"key":"ref3","article-title":"Addressee and response selection in multi-party conversations with speaker interaction rnns","author":"zhang","year":"2018","journal-title":"Association for the Advancement of Artificial Intelligence"},{"key":"ref6","first-page":"3776","article-title":"Building end-to-end dialogue systems using generative hierarchical neural network models","author":"serban","year":"2016","journal-title":"Association for the Advancement of Artificial Intelligence"},{"key":"ref5","article-title":"Multi-task learning for speaker-role adaptation in neural conversation models","author":"luan","year":"2017","journal-title":"International Joint Conference on Natural Language Processing"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2017.08.010"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.10.065"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D16-1231"},{"key":"ref9","article-title":"Sequence to sequence learning with neural networks","author":"hya","year":"2014","journal-title":"Conference on Advances in Neural Information Processing Systems"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-013-0503-z"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-5505"},{"key":"ref22","first-page":"163","article-title":"Speaker role contextual modeling for language understanding and dialogue policy learning","author":"chi","year":"2017","journal-title":"Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2 Short Papers) Asian Federation of Natural Language Processing"},{"key":"ref21","article-title":"Learning Context-Sensitive Time-Decay Attention for Role-Based Dialogue Modeling","author":"su","year":"2018","journal-title":"arXiv 1809 01557 [cs]"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1094"},{"key":"ref23","article-title":"A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues","author":"shi","year":"2018","journal-title":"Association for the Advancement of Artificial Intelligence (AAAI)"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1177\/00238309010440020101","article-title":"Disfluency rates in conversation: Effects of age, relationship, topic, role, and gender","volume":"44","author":"heather","year":"2001","journal-title":"Language and Speech"}],"event":{"name":"2019 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2019,7,14]]},"location":"Budapest, Hungary","end":{"date-parts":[[2019,7,19]]}},"container-title":["2019 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8840768\/8851681\/08852308.pdf?arnumber=8852308","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T19:15:45Z","timestamp":1674587745000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8852308\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1109\/ijcnn.2019.8852308","relation":{},"subject":[],"published":{"date-parts":[[2019,7]]}}}