{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:07:18Z","timestamp":1772122038369,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"28","license":[{"start":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T00:00:00Z","timestamp":1690329600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T00:00:00Z","timestamp":1690329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00521-023-08815-3","type":"journal-article","created":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T08:02:23Z","timestamp":1690358543000},"page":"20855-20867","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Interpreting open-domain dialogue generation by disentangling latent feature representations"],"prefix":"10.1007","volume":"35","author":[{"given":"Ye","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jingbo","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Guoyin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaoxia","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,26]]},"reference":[{"issue":"1","key":"8815_CR1","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1162\/089120104773633420","volume":"30","author":"JH Moor","year":"2004","unstructured":"Moor JH (2004) The turing test: The elusive standard of artificial intelligence. Comput Linguist 30(1):115\u2013116","journal-title":"Comput Linguist"},{"key":"8815_CR2","doi-asserted-by":"crossref","unstructured":"Madotto A, Wu C, Fung P (2018) Mem2seq: Effectively incorporating knowledge bases into end-to-end task-oriented dialog systems. In: Proceedings of the 56th annual meeting of the association for computational linguistics, pp 1468\u20131478","DOI":"10.18653\/v1\/P18-1136"},{"key":"8815_CR3","doi-asserted-by":"crossref","unstructured":"Qin L, Xu X, Che W, Zhang Y, Liu T (2020) Dynamic fusion network for multi-domain end-to-end task-oriented dialog. In: Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, Online, July 5\u201310, 2020, pp 6344\u20136354","DOI":"10.18653\/v1\/2020.acl-main.565"},{"key":"8815_CR4","doi-asserted-by":"crossref","unstructured":"Liu Q, Chen Y, Chen B, Lou J, Chen Z, Zhou B, Zhang D (2020) You impress me: dialogue generation via mutual persona perception. In: Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, Online, July 5\u201310, 2020, pp 1417\u20131427","DOI":"10.18653\/v1\/2020.acl-main.131"},{"key":"8815_CR5","doi-asserted-by":"crossref","unstructured":"Gangadharaiah R, Narayanaswamy B (2020) Recursive template-based frame generation for task oriented dialog. In: Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, Online, July 5\u201310, 2020, pp 2059\u20132064","DOI":"10.18653\/v1\/2020.acl-main.186"},{"key":"8815_CR6","doi-asserted-by":"crossref","unstructured":"Saha T, Patra AP, Saha S, Bhattacharyya P (2020) Towards emotion-aided multi-modal dialogue act classification. In: Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, Online, July 5\u201310, 2020, pp 4361\u20134372","DOI":"10.18653\/v1\/2020.acl-main.402"},{"key":"8815_CR7","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems 27: annual conference on neural information processing systems 2014, pp 3104\u20133112"},{"key":"8815_CR8","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems 30: annual conference on neural information processing systems, pp 5998\u20136008"},{"key":"8815_CR9","unstructured":"Kingma DP, Welling M (2014) Auto-encoding variational bayes. In: 2nd international conference on learning representations"},{"key":"8815_CR10","unstructured":"Sohn K, Lee H, Yan X (2015) Learning structured output representation using deep conditional generative models. In: Advances in neural information processing systems 28: annual conference on neural information processing systems 2015, pp 3483\u20133491"},{"key":"8815_CR11","doi-asserted-by":"crossref","unstructured":"Wang Y, Liao J, Yu H, Leng J (2022) Semantic-aware conditional variational autoencoder for one-to-many dialogue generation. Neural Comput Appl 1\u201313","DOI":"10.1007\/s00521-022-07182-9"},{"key":"8815_CR12","unstructured":"Shi W, Zhou H, Miao N, Li L (2020) Dispersed exponential family mixture vaes for interpretable text generation. In: Proceedings of the 37th international conference on machine learning vol 119, 8840\u20138851"},{"key":"8815_CR13","doi-asserted-by":"crossref","unstructured":"Bowman SR, Vilnis L, Vinyals O, Dai AM, J\u00f3zefowicz R, Bengio S (2016) Generating sentences from a continuous space. In: Proceedings of the 20th SIGNLL conference on computational natural language learning, pp 10\u201321 ( 2016)","DOI":"10.18653\/v1\/K16-1002"},{"key":"8815_CR14","doi-asserted-by":"crossref","unstructured":"Zhao T, Lee K, Esk\u00e9nazi M (2018) Unsupervised discrete sentence representation learning for interpretable neural dialog generation. In: Proceedings of the 56th annual meeting of the association for computational linguistics, pp 1098\u20131107","DOI":"10.18653\/v1\/P18-1101"},{"key":"8815_CR15","doi-asserted-by":"crossref","unstructured":"Serban IV, Sordoni A, Lowe R, Charlin L, Pineau J, Courville AC, Bengio Y (2017) A hierarchical latent variable encoder-decoder model for generating dialogues. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, February 4\u20139, 2017, San Francisco, California, USA, pp 3295\u2013 3301","DOI":"10.1609\/aaai.v31i1.10983"},{"key":"8815_CR16","doi-asserted-by":"crossref","unstructured":"Zhao T, Zhao R, Esk\u00e9nazi M (2017) Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. In: Proceedings of the 55th annual meeting of the association for computational linguistics, pp 654\u2013664","DOI":"10.18653\/v1\/P17-1061"},{"key":"8815_CR17","doi-asserted-by":"crossref","unstructured":"Gao J, Bi W, Liu X, Li J, Zhou G, Shi S (2019) A discrete CVAE for response generation on short-text conversation. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp 1898\u20131908","DOI":"10.18653\/v1\/D19-1198"},{"key":"8815_CR18","unstructured":"Pang B, Wu YN (2021) Latent space energy-based model of symbol-vector coupling for text generation and classification. In: Proceedings of the 38th international conference on machine learning, vol 139, pp 8359\u20138370"},{"key":"8815_CR19","unstructured":"Higgins I, Matthey L, Pal A, Burgess CP, Glorot X, Botvinick MM, Mohamed S, Lerchner A (2017) beta-vae: Learning basic visual concepts with a constrained variational framework. In: 5th international conference on learning representations, ICLR 2017, Toulon, France, April 24\u201326, 2017, Conference Track Proceedings"},{"key":"8815_CR20","unstructured":"Mathieu E, Rainforth T, Siddharth N, Teh YW (2019) Disentangling disentanglement in variational autoencoders. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th international conference on machine learning, ICML 2019, 9\u201315 June 2019, Long Beach, California, USA. Proceedings of Machine Learning Research 97:4402\u20134412"},{"key":"8815_CR21","doi-asserted-by":"crossref","unstructured":"Chen S, Yan J, Su Y, Wang YF (2021) Representation decomposition for image manipulation and beyond. In: 2021 IEEE international conference on image processing, ICIP 2021, Anchorage, AK, USA, September 19-22, 2021, pp 1169\u20131173","DOI":"10.1109\/ICIP42928.2021.9506015"},{"key":"8815_CR22","unstructured":"Li Y, Su H, Shen X, Li W, Cao Z, Niu S (2017) Dailydialog: a manually labelled multi-turn dialogue dataset. In: Proceedings of the eighth international joint conference on natural language processing, pp 986\u2013 995"},{"key":"8815_CR23","doi-asserted-by":"crossref","unstructured":"Rashkin H, Smith EM, Li M, Boureau Y (2019) Towards empathetic open-domain conversation models: A new benchmark and dataset. In: Proceedings of the 57th conference of the association for computational linguistics, ACL 2019, Florence, Italy, July 28\u2013August 2, 2019, Volume 1: Long Papers, pp 5370\u20135381","DOI":"10.18653\/v1\/P19-1534"},{"key":"8815_CR24","doi-asserted-by":"crossref","unstructured":"Papineni K, Roukos S, Ward T, Zhu W (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the association for computational linguistics, pp 311\u2013318","DOI":"10.3115\/1073083.1073135"},{"key":"8815_CR25","unstructured":"Lin C-Y (2004) Rouge: A package for automatic evaluation of summaries. In: Text summarization branches out, pp 74\u201381"},{"key":"8815_CR26","unstructured":"Banerjee S, Lavie A (2005) METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the workshop on intrinsic and extrinsic evaluation measures for machine translation and\/or summarization@ACL 2005, pp 65\u201372"},{"key":"8815_CR27","doi-asserted-by":"crossref","unstructured":"Li J, Galley M, Brockett C, Gao J, Dolan B (2016) A diversity-promoting objective function for neural conversation models. In: The 2016 conference of the north american chapter of the association for computational linguistics: human language technologies, pp 110\u2013119","DOI":"10.18653\/v1\/N16-1014"},{"key":"8815_CR28","doi-asserted-by":"crossref","unstructured":"Luong T, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 1412\u20131421","DOI":"10.18653\/v1\/D15-1166"},{"key":"8815_CR29","doi-asserted-by":"crossref","unstructured":"Wang Y, Wang H, Zhang X, Chaspari T, Choe Y, Lu M (2019) An attention-aware bidirectional multi-residual recurrent neural network (abmrnn): A study about better short-term text classification. In: IEEE international conference on acoustics, speech and signal processing, ICASSP 2019, Brighton, United Kingdom, May 12\u201317, 2019, pp 3582\u20133586","DOI":"10.1109\/ICASSP.2019.8682565"},{"key":"8815_CR30","doi-asserted-by":"crossref","unstructured":"Jiang Z, Zheng Y, Tan H, Tang B, Zhou H (2017) Variational deep embedding: an unsupervised and generative approach to clustering. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, pp 1965\u20131972","DOI":"10.24963\/ijcai.2017\/273"},{"key":"8815_CR31","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.neucom.2019.10.068","volume":"385","author":"Y Wang","year":"2020","unstructured":"Wang Y, Zhang X, Lu M, Wang H, Choe Y (2020) Attention augmentation with multi-residual in bidirectional LSTM. Neurocomputing 385:340\u2013347","journal-title":"Neurocomputing"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08815-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08815-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08815-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:20:35Z","timestamp":1693354835000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08815-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,26]]},"references-count":31,"journal-issue":{"issue":"28","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8815"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08815-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,26]]},"assertion":[{"value":"18 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}