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Process."],"published-print":{"date-parts":[[2020,11,30]]},"abstract":"<jats:p>In this article, conditional-transforming variational autoencoders (CTVAEs) are proposed for generating diverse short text conversations. In conditional variational autoencoders (CVAEs), the prior distribution of latent variable z follows a multivariate Gaussian distribution with mean and variance modulated by the input conditions. Previous work found that this distribution tended to become condition-independent in practical applications. Thus, this article designs CTVAEs to enhance the influence of conditions in CVAEs. In a CTVAE model, the latent variable z is sampled by performing a non-linear transformation on the combination of the input conditions and the samples from a condition-independent prior distribution N (0, I). In our experiments using a Chinese Sina Weibo dataset, the CTVAE model derives z samples for decoding with better condition-dependency than that of the CVAE model. The earth mover\u2019s distance (EMD) between the distributions of the latent variable z at the training stage, and the testing stage is also reduced by using the CTVAE model. In subjective preference tests, our proposed CTVAE model performs significantly better than CVAE and sequence-to-sequence (Seq2Seq) models on generating diverse, informative, and topic-relevant responses.<\/jats:p>","DOI":"10.1145\/3402884","type":"journal-article","created":{"date-parts":[[2020,10,13]],"date-time":"2020-10-13T11:47:34Z","timestamp":1602589654000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Condition-Transforming Variational Autoencoder for Generating Diverse Short Text Conversations"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9800-3271","authenticated-orcid":false,"given":"Yu-Ping","family":"Ruan","sequence":"first","affiliation":[{"name":"National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, P.R. China"}]},{"given":"Zhen-Hua","family":"Ling","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Speech and Language Information Processing, University of Science and Technology of China, Hefei, P.R. China"}]},{"given":"Xiaodan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Queen\u2019s University, Kingston, Canada"}]}],"member":"320","published-online":{"date-parts":[[2020,10,13]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2015 . Neural machine translation by jointly learning to align and translate . In Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015 , San Diego, CA, May 7\u20139 , 2015. http:\/\/arxiv.org\/abs\/1409.0473. 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