{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:24:04Z","timestamp":1742912644327,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031171192"},{"type":"electronic","value":"9783031171208"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-17120-8_29","type":"book-chapter","created":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T13:02:58Z","timestamp":1663938178000},"page":"364-376","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Adversarial Approach for\u00a0Unsupervised Syntax-Guided Paraphrase Generation"],"prefix":"10.1007","author":[{"given":"Tang","family":"Xue","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuran","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gongshen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"key":"29_CR1","unstructured":"Banerjee, S., Lavie, A.: Meteor: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and\/or Summarization (2005)"},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Bao, Y., et al: Generating sentences from disentangled syntactic and semantic spaces. In: Proceedings of ACL (2019)","DOI":"10.18653\/v1\/P19-1602"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Barzilay, R., Lee, L.: Learning to paraphrase: an unsupervised approach using multiple-sequence alignment. In: Proceedings of NAACL (2003)","DOI":"10.3115\/1073445.1073448"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Bowman, S., Vilnis, L., Vinyals, O., Dai, A., Jozefowicz, R., Bengio, S.: Generating sentences from a continuous space. In: Proceedings of CoNLL (2016)","DOI":"10.18653\/v1\/K16-1002"},{"key":"29_CR5","doi-asserted-by":"crossref","unstructured":"Chen, M., Tang, Q., Wiseman, S., Gimpel, K.: Controllable paraphrase generation with a syntactic exemplar. In: Proceedings of ACL (2019)","DOI":"10.18653\/v1\/P19-1599"},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Dolan, B., Quirk, C., Brockett, C.: Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources. In: Proceedings of COLING (2004)","DOI":"10.3115\/1220355.1220406"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Egonmwan, E., Chali, Y.: Transformer and seq2seq model for paraphrase generation. In: Proceedings of the 3rd Workshop on Neural Generation and Translation (2019)","DOI":"10.18653\/v1\/D19-5627"},{"key":"29_CR8","unstructured":"Fu, Y., Feng, Y., Cunningham, J.P.: Paraphrase generation with latent bag of words. Proceedings of NeurIPS (2019)"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Gao, S., Zhang, Y., Ou, Z., Yu, Z.: Paraphrase augmented task-oriented dialog generation. In: Proceedings of ACL (2020)","DOI":"10.18653\/v1\/2020.acl-main.60"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Goyal, T., Durrett, G.: Neural syntactic preordering for controlled paraphrase generation. In: Proceedings of ACL (2020)","DOI":"10.18653\/v1\/2020.acl-main.22"},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"Hu, J.E., Rudinger, R., Post, M., Van Durme, B.: ParaBank: monolingual bitext generation and sentential paraphrasing via lexically-constrained neural machine translation. In: Proceedings of AAAI (2019)","DOI":"10.1609\/aaai.v33i01.33016521"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Huang, K.H., Chang, K.W.: Generating syntactically controlled paraphrases without using annotated parallel pairs. In: Proceedings of EACL (2021)","DOI":"10.18653\/v1\/2021.eacl-main.88"},{"key":"29_CR13","doi-asserted-by":"crossref","unstructured":"Iyyer, M., Wieting, J., Gimpel, K., Zettlemoyer, L.: Adversarial example generation with syntactically controlled paraphrase networks. In: Proceedings of NAACL (2018)","DOI":"10.18653\/v1\/N18-1170"},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Kauchak, D., Barzilay, R.: Paraphrasing for automatic evaluation. In: Proceedings of AACL (2006)","DOI":"10.3115\/1220835.1220893"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Kumar, A., Ahuja, K., Vadapalli, R., Talukdar, P.: Syntax-guided controlled generation of paraphrases. Trans. Assoc. Comput. Linguist. (2020)","DOI":"10.1162\/tacl_a_00318"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Li, J., Monroe, W., Ritter, A., Jurafsky, D., Galley, M., Gao, J.: Deep reinforcement learning for dialogue generation. In: EMNLP (2016)","DOI":"10.18653\/v1\/D16-1127"},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Li, Z., Jiang, X., Shang, L., Li, H.: Paraphrase generation with deep reinforcement learning. In: Proceedings of EMNLP (2018)","DOI":"10.18653\/v1\/D18-1421"},{"key":"29_CR18","doi-asserted-by":"crossref","unstructured":"Lin, Z., Wan, X.: Pushing paraphrase away from original sentence: a multi-round paraphrase generation approach. In: Proceedings of ACL Findings (2021)","DOI":"10.18653\/v1\/2021.findings-acl.135"},{"key":"29_CR19","doi-asserted-by":"crossref","unstructured":"Liu, X., Mou, L., Meng, F., Zhou, H., Zhou, J., Song, S.: Unsupervised paraphrasing by simulated annealing. In: Proceedings of ACL (2020)","DOI":"10.18653\/v1\/2020.acl-main.28"},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"Ma, S., Sun, X., Wang, Y., Lin, J.: Bag-of-words as target for neural machine translation. In: Proceedings of ACL (2018)","DOI":"10.18653\/v1\/P18-2053"},{"key":"29_CR21","unstructured":"Madnani, N., Tetreault, J., Chodorow, M.: Re-examining machine translation metrics for paraphrase identification. In: Proceedings of NAACL (2012)"},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Mallinson, J., Sennrich, R., Lapata, M.: Paraphrasing revisited with neural machine translation. In: Proceedings of EACL (2017)","DOI":"10.18653\/v1\/E17-1083"},{"key":"29_CR23","unstructured":"McKeown, K.: Paraphrasing questions using given and new information. Am. J. Comput. Linguist. (1983)"},{"key":"29_CR24","doi-asserted-by":"crossref","unstructured":"Meng, Y., et al.: ConRPG: paraphrase generation using contexts as regularizer. In: Proceedings of EMNLP (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.199"},{"key":"29_CR25","unstructured":"Prakash, A., et al.: Neural paraphrase generation with stacked residual LSTM networks. In: Proceedings of COLING (2016)"},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Qian, L., Qiu, L., Zhang, W., Jiang, X., Yu, Y.: Exploring diverse expressions for paraphrase generation. In: Proceedings of EMNLP (2019)","DOI":"10.18653\/v1\/D19-1313"},{"key":"29_CR27","unstructured":"Ranzato, M., Chopra, S., Auli, M., Zaremba, W.: Sequence level training with recurrent neural networks. In: Proceedings of ICLR (2016)"},{"key":"29_CR28","doi-asserted-by":"crossref","unstructured":"Roy, A., Grangier, D.: Unsupervised paraphrasing without translation. In: Proceedings of ACL (2019)","DOI":"10.18653\/v1\/P19-1605"},{"key":"29_CR29","doi-asserted-by":"crossref","unstructured":"Siddique, A., Oymak, S., Hristidis, V.: Unsupervised paraphrasing via deep reinforcement learning. In: Proceedings of KDD (2020)","DOI":"10.1145\/3394486.3403231"},{"key":"29_CR30","doi-asserted-by":"crossref","unstructured":"Sun, J., Ma, X., Peng, N.: Aesop: Paraphrase generation with adaptive syntactic control. In: Proceedings of EMNLP (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.420"},{"key":"29_CR31","doi-asserted-by":"crossref","unstructured":"Tao, C., Gao, S., Li, J., Feng, Y., Zhao, D., Yan, R.: Learning to organize a bag of words into sentences with neural networks: an empirical study. In: Proceedings of NAACL (2021)","DOI":"10.18653\/v1\/2021.naacl-main.134"},{"key":"29_CR32","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Proceedings of NeurIPS (2017)"},{"key":"29_CR33","doi-asserted-by":"crossref","unstructured":"Wieting, J., Gimpel, K.: Paranmt-50m: pushing the limits of paraphrastic sentence embeddings with millions of machine translations. In: Proceedings of ACL (2018)","DOI":"10.18653\/v1\/P18-1042"},{"key":"29_CR34","doi-asserted-by":"crossref","unstructured":"Witteveen, S., Andrews, M.: Paraphrasing with large language models. In: Proceedings of the 3rd Workshop on Neural Generation and Translation (2019)","DOI":"10.18653\/v1\/D19-5623"},{"key":"29_CR35","unstructured":"Wubben, S., Van Den Bosch, A., Krahmer, E.: Paraphrase generation as monolingual translation: Data and evaluation. In: Proceedings of the 6th International Natural Language Generation Conference (2010)"},{"key":"29_CR36","doi-asserted-by":"crossref","unstructured":"Yang, X., Liu, Y., Xie, D., Wang, X., Balasubramanian, N.: Latent part-of-speech sequences for neural machine translation. In: Proceedings of EMNLP (2019)","DOI":"10.18653\/v1\/D19-1072"},{"key":"29_CR37","doi-asserted-by":"publisher","unstructured":"Yuan, W., Ding, L., Meng, K., Liu, G.: Text generation with syntax - enhanced variational autoencoder. In: International Joint Conference on Neural Networks, IJCNN 2021, Shenzhen, China, 18\u201322 July 2021, pp. 1\u20138. IEEE (2021). https:\/\/doi.org\/10.1109\/IJCNN52387.2021.9533865","DOI":"10.1109\/IJCNN52387.2021.9533865"},{"key":"29_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yang, Y., Yuan, S., Shen, D., Carin, L.: Syntax-infused variational autoencoder for text generation. In: Proceedings of ACL (2019)","DOI":"10.18653\/v1\/P19-1199"},{"key":"29_CR39","doi-asserted-by":"crossref","unstructured":"Zhao, S., Meng, R., He, D., Saptono, A., Parmanto, B.: Integrating transformer and paraphrase rules for sentence simplification. In: Proceedings of EMNLP (2018)","DOI":"10.18653\/v1\/D18-1355"},{"key":"29_CR40","unstructured":"Zhao, S., Wang, H., Lan, X., Liu, T.: Leveraging multiple MT engines for paraphrase generation. In: Proceedings of COLING (2010)"}],"container-title":["Lecture Notes in Computer Science","Natural Language Processing and Chinese Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-17120-8_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,23]],"date-time":"2022-09-23T13:06:59Z","timestamp":1663938419000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-17120-8_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031171192","9783031171208"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-17120-8_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NLPCC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF International Conference on Natural Language Processing and Chinese Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guilin","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nlpcc2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/tcci.ccf.org.cn\/conference\/2022\/","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":"Softconf","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"327","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":"73","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":"0","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":"22% - 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":"1.5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}