{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T22:06:52Z","timestamp":1742940412214,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030630300"},{"type":"electronic","value":"9783030630317"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-63031-7_34","type":"book-chapter","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T00:05:35Z","timestamp":1605139535000},"page":"467-479","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["WAE$$_{-}$$RN: Integrating Wasserstein Autoencoder and Relational Network for Text Sequence"],"prefix":"10.1007","author":[{"given":"Xinxin","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Xiaoming","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Guan","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Fangfang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Weiguang","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"issue":"6088","key":"34_CR1","first-page":"696","volume":"323","author":"DE Rumelhart","year":"1988","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J., et al.: Learning representations by back-propagating errors. Nature 323(6088), 696\u2013699 (1988)","journal-title":"Nature"},{"key":"34_CR2","unstructured":"Bahdanau, D., Cho, K., Bengio, Y., et al.: Neural machine translation by jointly learning to align and translate. In: International Conference on Learning Representations (2015)"},{"key":"34_CR3","doi-asserted-by":"publisher","unstructured":"Luong, M., Pham, H., Manning, C.D., et al.: Effective approaches to attention-based neural machine translation. In: Empirical Methods in Natural Language Processing, pp. 1412\u20131421 (2015). https:\/\/doi.org\/10.18653\/v1\/d15-1166","DOI":"10.18653\/v1\/d15-1166"},{"key":"34_CR4","doi-asserted-by":"publisher","unstructured":"Klein, T., Nabi, M.: Attention is (not) all you need for commonsense reasoning. In: Meeting of the Association for Computational Linguistics, pp. 4831\u20134836 (2019). https:\/\/doi.org\/10.18653\/v1\/p19-1477","DOI":"10.18653\/v1\/p19-1477"},{"key":"34_CR5","doi-asserted-by":"crossref","unstructured":"Tan, Z., Wang, M., Xie, J., et al.: Deep semantic role labeling with self-attention. In: National Conference on Artificial Intelligence, pp. 4929\u20134936 (2018)","DOI":"10.1609\/aaai.v32i1.11928"},{"key":"34_CR6","unstructured":"Santoro, A., Raposo, D., Barrett, D.G., et al.: A simple neural network module for relational reasoning. In: Neural Information Processing Systems, pp. 4967\u20134976 (2017)"},{"key":"34_CR7","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: International Conference on Learning Representations (2014)"},{"key":"34_CR8","doi-asserted-by":"publisher","unstructured":"Peters, M.E., Neumann, M., Iyyer, M., et al.: Deep contextualized word representations. In: North American Chapter of the Association for Computational Linguistics, pp. 2227\u20132237 (2018). https:\/\/doi.org\/10.18653\/v1\/n18-1202","DOI":"10.18653\/v1\/n18-1202"},{"key":"34_CR9","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M., Lee, K., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. In: North American Chapter of the Association for Computational Linguistics, pp. 4171\u20134186 (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"34_CR10","unstructured":"Lan, Z., Chen, M., Goodman, S., et al.: ALBERT: A Lite BERT for self-supervised learning of language representations. In: International Conference on Learning Representations (2020)"},{"key":"34_CR11","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Han, X., Liu, Z., et al.: ERNIE: enhanced language representation with informative entities. In: Meeting of the Association for Computational Linguistics, pp. 1441\u20131451 (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"34_CR12","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, S., Li, Y., et al.: ERNIE 2.0: a continual pre-training framework for language understanding. arXiv: Computation and Language (2019)","DOI":"10.1609\/aaai.v34i05.6428"},{"key":"34_CR13","unstructured":"Yang, Z., Dai, Z., Yang, Y., et al.: XLNet: generalized autoregressive pretraining for language understanding. arXiv: Computation and Language (2019)"},{"key":"34_CR14","doi-asserted-by":"crossref","unstructured":"Bowman, S.R., Vilnis, L., Vinyals, O., et al.: Generating sentences from a continuous space. In: Conference on Computational Natural Language Learning, pp. 10\u201321 (2016). DOIurlhttp:\/\/doi.org\/10.18653\/v1\/k16-1002","DOI":"10.18653\/v1\/K16-1002"},{"key":"34_CR15","unstructured":"Tolstikhin, I., Bousquet, O., Gelly, S., et al.: Wasserstein auto-encoders. In: International Conference on Learning Representations (2018)"},{"key":"34_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, B., Xiong, D., Su, J., et al.: Variational neural machine translation. In: Empirical Methods in Natural Language Processing, pp. 521\u2013530 (2016)","DOI":"10.18653\/v1\/D16-1050"},{"key":"34_CR17","unstructured":"Shah, H., Barber, D.: Generative neural machine translation. In: Neural Information Processing Systems, pp. 1346\u20131355 (2018)"},{"key":"34_CR18","doi-asserted-by":"publisher","unstructured":"Bahuleyan, H., Mou L., Zhou, H., et al.: Stochastic wasserstein autoencoder for probabilistic sentence generation. In: North American Chapter of the Association for Computational Linguistics, pp. 4068\u20134076 (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1411","DOI":"10.18653\/v1\/n19-1411"},{"key":"34_CR19","doi-asserted-by":"publisher","unstructured":"Wang, P.Z., Wang, W.Y.: Riemannian normalizing flow on variational wasserstein autoencoder for text modeling. In: North American Chapter of the Association for Computational Linguistics, pp. 284\u2013294 (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1025","DOI":"10.18653\/v1\/n19-1025"},{"key":"34_CR20","unstructured":"Zhang, W., Jiawei, H., Feng, Y., et al.: Refining source representations with relation networks for neural machine translation. In: International Conference on Computational Linguistics, pp. 1292\u20131303 (2018)"},{"key":"34_CR21","doi-asserted-by":"publisher","unstructured":"Chen, H., Lin, Z., Ding, G., et al.: GRN: gated relation network to enhance convolutional neural network for named entity recognition. In: National Conference on Artificial Intelligence, vol. 33, no. 01, pp. 6236\u20136243 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33016236","DOI":"10.1609\/aaai.v33i01.33016236"},{"key":"34_CR22","unstructured":"Pradhan, S., Moschitti, A., Xe, N., et al.: CoNLL-2012 shared task: modeling multilingual unrestricted coreference in OntoNotes. In: Empirical Methods in Natural Language Processing, pp. 1\u201340 (2012)"},{"key":"34_CR23","unstructured":"Ranzato, M., Chopra, S., Auli, M., et al.: Sequence level training with recurrent neural networks. In: International Conference on Learning Representations (2016)"},{"key":"34_CR24","unstructured":"Shu, R., Nakayama, H.: Compressing word embeddings via deep compositional code learning. In: International Conference on Learning Representations (2018)"},{"key":"34_CR25","unstructured":"Huang, P., Wang, C., Huang, S., et al.: Towards neural phrase-based machine translation. In: International Conference on Learning Representations (2018)"},{"key":"34_CR26","doi-asserted-by":"publisher","unstructured":"Eikema, B., Aziz, W.: Auto-encoding variational neural machine translation. In: Meeting of the Association for Computational Linguistics, pp. 124\u2013141 (2019). https:\/\/doi.org\/10.18653\/v1\/w19-4315","DOI":"10.18653\/v1\/w19-4315"},{"key":"34_CR27","unstructured":"Pradhan, S., Moschitti, A., Xue, N., et al.: Towards robust linguistic analysis using OntoNotes. In: Conference on Computational Natural Language Learning, pp. 143\u2013152 (2013)"}],"container-title":["Lecture Notes in Computer Science","Chinese Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-63031-7_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T19:05:52Z","timestamp":1710270352000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-63031-7_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030630300","9783030630317"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-63031-7_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"12 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China National Conference on Chinese Computational Linguistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hainan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cncl2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cips-cl.org\/static\/CCL2020\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"www.softconf.com","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"99","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":"32","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":"2","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":"32% - 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":"3","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)"}}]}}