{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:27:08Z","timestamp":1742912828822,"version":"3.40.3"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030863821"},{"type":"electronic","value":"9783030863838"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86383-8_24","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T08:02:49Z","timestamp":1631260969000},"page":"297-308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Latent Variable Model with Hierarchical Structure and GPT-2 for Long Text Generation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2293-9209","authenticated-orcid":false,"given":"Kun","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohui","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongwang","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"24_CR1","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. ArXiv abs\/1701.07875 (2017)"},{"key":"24_CR2","unstructured":"Bahuleyan, H., Mou, L., Vechtomova, O., Poupart, P.: Variational attention for sequence-to-sequence models. In: COLING (2018)"},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Bengio, S.: Generating sentences from a continuous space. Computer Science (2015)","DOI":"10.18653\/v1\/K16-1002"},{"key":"24_CR4","unstructured":"Burda, Y., Grosse, R.B., Salakhutdinov, R.: Importance weighted autoencoders. CoRR abs\/1509.00519 (2016)"},{"key":"24_CR5","unstructured":"Chen, L., et al.: Adversarial text generation via feature-mover\u2019s distance. ArXiv abs\/1809.06297 (2018)"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Chen, M., Tang, Q., Livescu, K., Gimpel, K.: Variational sequential labelers for semi-supervised learning. In: EMNLP (2018)","DOI":"10.18653\/v1\/D18-1020"},{"key":"24_CR7","unstructured":"Chung, J., \u00c7aglar G\u00fcl\u00e7ehre, Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. ArXiv abs\/1412.3555 (2014)"},{"key":"24_CR8","unstructured":"Deng, Y., Kim, Y., Chiu, J.T., Guo, D., Rush, A.M.: Latent alignment and variational attention. In: NeurIPS (2018)"},{"key":"24_CR9","unstructured":"Dieng, A.B., Kim, Y., Rush, A.M., Blei, D.: Avoiding latent variable collapse with generative skip models. ArXiv abs\/1807.04863 (2019)"},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Fang, L., Li, C., Gao, J., Dong, W., Chen, C.: Implicit deep latent variable models for text generation (2019)","DOI":"10.18653\/v1\/D19-1407"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Fu, H., Li, C., Liu, X., Gao, J., \u00c7elikyilmaz, A., Carin, L.: Cyclical annealing schedule: a simple approach to mitigating kl vanishing. In: NAACL-HLT (2019)","DOI":"10.18653\/v1\/N19-1021"},{"key":"24_CR12","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1162\/tacl_a_00030","volume":"6","author":"K Guu","year":"2018","unstructured":"Guu, K., Hashimoto, T., Oren, Y., Liang, P.: Generating sentences by editing prototypes. Trans. Assoc. Comput. Linguist. 6, 437\u2013450 (2018)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"24_CR13","unstructured":"He, J., Spokoyny, D., Neubig, G., Berg-Kirkpatrick, T.: Lagging inference networks and posterior collapse in variational autoencoders. ArXiv abs\/1901.05534 (2019)"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Holtzman, A., Buys, J., Forbes, M., Bosselut, A., Choi, Y.: Learning to write with cooperative discriminators (2018)","DOI":"10.18653\/v1\/P18-1152"},{"key":"24_CR15","unstructured":"Hu, Z., Yang, Z., Liang, X., Salakhutdinov, R., Xing, E.: Toward controlled generation of text. In: ICML (2017)"},{"key":"24_CR16","unstructured":"Kaiser, \u0141., et al.: Fast decoding in sequence models using discrete latent variables. In: ICML (2018)"},{"key":"24_CR17","unstructured":"Kim, Y., Wiseman, S., Miller, A.C., Sontag, D., Rush, A.M.: Semi-amortized variational autoencoders (2018)"},{"key":"24_CR18","unstructured":"Kingma, D.P., Salimans, T., Welling, M.: Improved variational inference with inverse autoregressive flow. ArXiv abs\/1606.04934 (2017)"},{"key":"24_CR19","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes (2014)"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Li, B., He, J., Neubig, G., Berg-Kirkpatrick, T., Yang, Y.: A surprisingly effective fix for deep latent variable modeling of text. ArXiv abs\/1909.00868 (2019)","DOI":"10.18653\/v1\/D19-1370"},{"key":"24_CR21","doi-asserted-by":"crossref","unstructured":"Li, C., et al.: Optimus: organizing sentences via pre-trained modeling of a latent space. ArXiv abs\/2004.04092 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.378"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Li, J., Luong, M.T., Jurafsky, D.: A hierarchical neural autoencoder for paragraphs and documents. ArXiv abs\/1506.01057 (2015)","DOI":"10.3115\/v1\/P15-1107"},{"key":"24_CR23","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.neunet.2019.07.001","volume":"119","author":"W Li","year":"2019","unstructured":"Li, W., Ding, W., Sadasivam, R., Cui, X., Chen, P.: His-GAN: a histogram-based GAN model to improve data generation quality. Neural Netw. 119, 31\u201345 (2019)","journal-title":"Neural Netw."},{"key":"24_CR24","doi-asserted-by":"crossref","unstructured":"Li, W., Fan, L., Wang, Z., Ma, C., Cui, X.: Tackling mode collapse in multi-generator GANs with orthogonal vectors. Pattern Recogn. 110, 107646 (2021)","DOI":"10.1016\/j.patcog.2020.107646"},{"key":"24_CR25","unstructured":"Li, W., Liang, Z., Ma, P., Wang, R., Cui, X., Chen, P.: Hausdorff GAN: improving GAN generation quality with Hausdorff metric. IEEE Trans. Cybern. PP, 1\u201313 (2021)"},{"key":"24_CR26","doi-asserted-by":"crossref","unstructured":"Liu, D., Liu, G.: A transformer-based variational autoencoder for sentence generation. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20137 (2019)","DOI":"10.1109\/IJCNN.2019.8852155"},{"key":"24_CR27","unstructured":"Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I.J.: Adversarial autoencoders. ArXiv abs\/1511.05644 (2015)"},{"key":"24_CR28","unstructured":"Miao, Y., Grefenstette, E., Blunsom, P.: Discovering discrete latent topics with neural variational inference. ArXiv abs\/1706.00359 (2017)"},{"key":"24_CR29","unstructured":"Miao, Y., Yu, L., Blunsom, P.: Neural variational inference for text processing. Comput. Sci. 1791\u20131799 (2016)"},{"key":"24_CR30","unstructured":"Mikolov, T., Karafi\u00e1t, M., Burget, L., Cernock, J., Khudanpur, S.: Recurrent neural network based language model. In: Interspeech, Conference of the International Speech Communication Association, Makuhari, Chiba, Japan, September 2015"},{"key":"24_CR31","doi-asserted-by":"crossref","unstructured":"Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: BLEU: a method for automatic evaluation of machine translation. In: ACL (2002)","DOI":"10.3115\/1073083.1073135"},{"key":"24_CR32","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners (2019)"},{"key":"24_CR33","unstructured":"Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models (2014)"},{"key":"24_CR34","doi-asserted-by":"crossref","unstructured":"Semeniuta, S., Severyn, A., Barth, E.: A hybrid convolutional variational autoencoder for text generation (2017)","DOI":"10.18653\/v1\/D17-1066"},{"key":"24_CR35","doi-asserted-by":"crossref","unstructured":"Serban, I., et al.: A hierarchical latent variable encoder-decoder model for generating dialogues. ArXiv abs\/1605.06069 (2017)","DOI":"10.1609\/aaai.v31i1.10983"},{"key":"24_CR36","unstructured":"Shah, H., Barber, D.: Generative neural machine translation. ArXiv abs\/1806.05138 (2018)"},{"key":"24_CR37","doi-asserted-by":"crossref","unstructured":"Shen, D., et al.: Towards generating long and coherent text with multi-level latent variable models. ArXiv abs\/1902.00154 (2019)","DOI":"10.18653\/v1\/P19-1200"},{"key":"24_CR38","doi-asserted-by":"crossref","unstructured":"Shen, X., et al.: A conditional variational framework for dialog generation. In: ACL (2017)","DOI":"10.18653\/v1\/P17-2080"},{"key":"24_CR39","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS (2014)"},{"key":"24_CR40","unstructured":"Vaswani, A., et al.: Attention is all you need. ArXiv abs\/1706.03762 (2017)"},{"key":"24_CR41","doi-asserted-by":"crossref","unstructured":"Wang, T., Wan, X.: T-CVAE: transformer-based conditioned variational autoencoder for story completion. In: IJCAI (2019)","DOI":"10.24963\/ijcai.2019\/727"},{"key":"24_CR42","doi-asserted-by":"crossref","unstructured":"Yang, S., Li, L., Wang, S., Zhang, W., Huang, Q., Tian, Q.: A structured latent variable recurrent network with stochastic attention for generating Weibo comments. In: IJCAI (2020)","DOI":"10.24963\/ijcai.2020\/548"},{"key":"24_CR43","unstructured":"Yang, Z., Hu, Z., Salakhutdinov, R., Berg-Kirkpatrick, T.: Improved variational autoencoders for text modeling using dilated convolutions. ArXiv abs\/1702.08139 (2017)"},{"key":"24_CR44","doi-asserted-by":"crossref","unstructured":"Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: HLT-NAACL (2016)","DOI":"10.18653\/v1\/N16-1174"},{"key":"24_CR45","doi-asserted-by":"crossref","unstructured":"Yin, P., Zhou, C., He, J., Neubig, G.: StructVAE: tree-structured latent variable models for semi-supervised semantic parsing. In: ACL (2018)","DOI":"10.18653\/v1\/P18-1070"},{"key":"24_CR46","doi-asserted-by":"crossref","unstructured":"Yu, L., Zhang, W., Wang, J., Yu, Y.: SeqGAN: sequence generative adversarial nets with policy gradient. In: AAAI (2017)","DOI":"10.1609\/aaai.v31i1.10804"},{"key":"24_CR47","unstructured":"Zhang, Y., et al.: Adversarial feature matching for text generation. In: ICML (2017)"},{"key":"24_CR48","unstructured":"Zhao, J., Kim, Y., Zhang, K., Rush, A.M., LeCun, Y.: Adversarially regularized autoencoders. In: ICML (2018)"},{"key":"24_CR49","doi-asserted-by":"crossref","unstructured":"Zhao, T., Zhao, R., Eskenazi, M.: Learning discourse-level diversity for neural dialog models using conditional variational autoencoders (2017)","DOI":"10.18653\/v1\/P17-1061"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86383-8_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T14:44:15Z","timestamp":1709822655000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86383-8_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030863821","9783030863838"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86383-8_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"496","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"265","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}