{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T14:54:12Z","timestamp":1780930452422,"version":"3.54.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:00:00Z","timestamp":1689552000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T00:00:00Z","timestamp":1689552000000},"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 &amp; Applic"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s00521-023-08830-4","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T11:02:12Z","timestamp":1689591732000},"page":"7705-7719","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Improving paraphrase generation using supervised neural-based statistical machine translation framework"],"prefix":"10.1007","volume":"37","author":[{"given":"Abdur","family":"Razaq","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Babar","family":"Shah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gohar","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar","family":"Alfandi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abrar","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahid","family":"Halim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atta","family":"Ur Rahman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,17]]},"reference":[{"key":"8830_CR1","doi-asserted-by":"crossref","unstructured":"Wang S, Gupta R, Chang N, Baldridge J (2019) A task in a suit and a tie: paraphrase generation with semantic augmentation. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, no 01, pp 7176\u20137183","DOI":"10.1609\/aaai.v33i01.33017176"},{"key":"8830_CR2","doi-asserted-by":"crossref","unstructured":"Gupta A, Agarwal A, Singh P, Rai P (2018) A deep generative framework for paraphrase generation. In: Proceedings of the AAAI conference on artificial intelligence, vol 32, no 1","DOI":"10.1609\/aaai.v32i1.11956"},{"issue":"3","key":"8830_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2483669.2483672","volume":"4","author":"Y Marton","year":"2013","unstructured":"Marton Y (2013) Distributional phrasal paraphrase generation for statistical machine translation. ACM Trans Intell Syst Technol 4(3):1\u201332","journal-title":"ACM Trans Intell Syst Technol"},{"key":"8830_CR4","unstructured":"Sun H, Zhou M (2012) Joint learning of a dual SMT system for paraphrase generation. In: Proceedings of the 50th annual meeting of the association for computational linguistics, vol 2, pp 38\u201342"},{"key":"8830_CR5","first-page":"9","volume":"2010","author":"J Chevelu","year":"2010","unstructured":"Chevelu J, Putois G, Lepage Y (2010) The true score of statistical paraphrase generation. Coling 2010:9","journal-title":"Coling"},{"issue":"3","key":"8830_CR6","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1162\/coli_a_00002","volume":"36","author":"N Madnani","year":"2010","unstructured":"Madnani N, Dorr BJ (2010) Generating phrasal and sentential paraphrases: a survey of data-driven methods. Comput Linguist 36(3):341\u2013387","journal-title":"Comput Linguist"},{"key":"8830_CR7","doi-asserted-by":"crossref","unstructured":"Cao D, Xu L (2016) Analysis of complex network methods for extractive automatic text summarization. In: 2016 2nd IEEE international conference on computer and communications, pp 2749\u20132756","DOI":"10.1109\/CompComm.2016.7925198"},{"key":"8830_CR8","doi-asserted-by":"crossref","unstructured":"Berant J, Liang P (2014) Semantic parsing via paraphrasing. In\u201d Proceedings of the 52nd annual meeting of the association for computational linguistics, vol 1, pp 1415\u20131425","DOI":"10.3115\/v1\/P14-1133"},{"key":"8830_CR9","doi-asserted-by":"crossref","unstructured":"Li Z, Jiang X, Shang L, Liu Q (2019) Decomposable neural paraphrase generation. arXiv preprint https:\/\/arxiv.org\/abs\/1906.09741","DOI":"10.18653\/v1\/P19-1332"},{"key":"8830_CR10","unstructured":"Utiyama M, Isahara H (2007) A comparison of pivot methods for phrase-based statistical machine translation. In: Human language technologies 2007: the conference of the north American chapter of the association for computational linguistics; proceedings of the main conference, pp 484\u2013491"},{"key":"8830_CR11","doi-asserted-by":"crossref","unstructured":"Roy A, Grangier D (2019) Unsupervised paraphrasing without translation. arXiv preprint https:\/\/arxiv.org\/abs\/1905.12752","DOI":"10.18653\/v1\/P19-1605"},{"key":"8830_CR12","unstructured":"Prakash A, Hasan SA, Lee K, Datla V, Qadir A, Liu J, Farri O (2016) Neural paraphrase generation with stacked residual LSTM networks. arXiv preprint https:\/\/arxiv.org\/abs\/1610.03098"},{"key":"8830_CR13","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, p 27"},{"key":"8830_CR14","doi-asserted-by":"crossref","unstructured":"Devlin J, Zbib R, Huang Z, Lamar T, Schwartz R, Makhoul J (2014) Fast and robust neural network joint models for statistical machine translation. In: Proceedings of the 52nd annual meeting of the association for computational linguistics, vol 1, pp 1370\u20131380","DOI":"10.3115\/v1\/P14-1129"},{"key":"8830_CR15","doi-asserted-by":"crossref","unstructured":"Cho K, Van Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation. arXiv preprint https:\/\/arxiv.org\/abs\/1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"issue":"9","key":"8830_CR16","doi-asserted-by":"publisher","first-page":"e02504","DOI":"10.1016\/j.heliyon.2019.e02504","volume":"5","author":"D Banik","year":"2019","unstructured":"Banik D, Ekbal A, Bhattacharyya P, Bhattacharyya S, Platos J (2019) Statistical-based system combination approach to gain advantages over different machine translation systems. Heliyon 5(9):e02504","journal-title":"Heliyon"},{"key":"8830_CR17","unstructured":"Luong MT, Manning CD (2015) Stanford neural machine translation systems for spoken language domains. In: Proceedings of the 12th international workshop on spoken language translation: evaluation campaign, pp 76\u201379"},{"key":"8830_CR18","doi-asserted-by":"crossref","unstructured":"Qiu D, Chen L, Yu Y (2022) Document-level paraphrase generation base on attention enhanced graph LSTM. In: Applied intelligence, pp 1\u201313","DOI":"10.1007\/s10489-022-04031-z"},{"key":"8830_CR19","doi-asserted-by":"crossref","unstructured":"Hu JE, Singh A, Holzenberger N, Post M, Van Durme B (2019) Large-scale, diverse, paraphrastic bitexts via sampling and clustering. In: Proceedings of the 23rd conference on computational natural language learning (CoNLL), pp 44\u201354","DOI":"10.18653\/v1\/K19-1005"},{"key":"8830_CR20","unstructured":"Quirk C, Brockett C, Dolan B (2004) Monolingual machine translation for paraphrase generation. Microsoft research"},{"key":"8830_CR21","doi-asserted-by":"crossref","unstructured":"Zhao S, Lan X, Liu T, Li S (2009) Application-driven statistical paraphrase generation. In: Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP, pp 834\u2013842","DOI":"10.3115\/1690219.1690263"},{"key":"8830_CR22","doi-asserted-by":"crossref","unstructured":"Nguyen-Ngoc K, Le AC, Nguyen VH (2018) An attention-based long-short-term-memory model for paraphrase generation. In: Integrated uncertainty in knowledge modelling and decision making: 6th international symposium, IUKM 2018, Hanoi, Vietnam, vol 6, pp 166\u2013178","DOI":"10.1007\/978-3-319-75429-1_14"},{"key":"8830_CR23","doi-asserted-by":"crossref","unstructured":"Gadag A, Sagar BM (2016) A review on different methods of paraphrasing. In: 2016 International conference on electrical, electronics, communication, computer and optimization techniques (ICEECCOT), pp 188\u2013191","DOI":"10.1109\/ICEECCOT.2016.7955212"},{"issue":"1","key":"8830_CR24","first-page":"1","volume":"9","author":"K McKeown","year":"1983","unstructured":"McKeown K (1983) Paraphrasing questions using given and new information. Am J Comput Linguist 9(1):1\u201310","journal-title":"Am J Comput Linguist"},{"issue":"4","key":"8830_CR25","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1017\/S1351324901002765","volume":"7","author":"D Lin","year":"2001","unstructured":"Lin D, Pantel P (2001) Discovery of inference rules for question-answering. Nat Lang Eng 7(4):343\u2013360","journal-title":"Nat Lang Eng"},{"key":"8830_CR26","unstructured":"Fu Y, Feng Y, Cunningham JP (2019) Paraphrase generation with latent bag of words. In: Advances in neural information processing systems, p 32"},{"key":"8830_CR27","doi-asserted-by":"crossref","unstructured":"Bolshakov IA, Gelbukh A (2004) Synonymous paraphrasing using wordnet and internet. In: Natural language processing and information systems: 9th international conference on applications of natural language to information systems, NLDB 2004, Salford, UK, vol 9, pp 312\u2013323","DOI":"10.1007\/978-3-540-27779-8_27"},{"key":"8830_CR28","doi-asserted-by":"crossref","unstructured":"Qian L, Qiu L, Zhang W, Jiang X, Yu Y (2019) Exploring diverse expressions for paraphrase generation. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 3173\u20133182","DOI":"10.18653\/v1\/D19-1313"},{"key":"8830_CR29","doi-asserted-by":"crossref","unstructured":"Goyal T, Durrett G (2020) Neural syntactic preordering for controlled paraphrase generation. arXiv preprint https:\/\/arxiv.org\/abs\/2005.02013","DOI":"10.18653\/v1\/2020.acl-main.22"},{"key":"8830_CR30","doi-asserted-by":"crossref","unstructured":"Sennrich R, Haddow B, Birch A (2015) Improving neural machine translation models with monolingual data. arXiv preprint https:\/\/arxiv.org\/abs\/1511.06709","DOI":"10.18653\/v1\/P16-1009"},{"key":"8830_CR31","unstructured":"Lample G, Conneau A, Denoyer L, Ranzato MA (2017) Unsupervised machine translation using monolingual corpora only. arXiv preprint https:\/\/arxiv.org\/abs\/1711.00043"},{"key":"8830_CR32","unstructured":"Sun X, Tian Y, Meng Y, Peng N, Wu F, Li J, Fan C (2021) Paraphrase generation as unsupervised machine translation. arXiv preprint https:\/\/arxiv.org\/abs\/2109.02950"},{"key":"8830_CR33","unstructured":"Sokolov A, Filimonov D (2020) Neural machine translation for paraphrase generation. arXiv preprint https:\/\/arxiv.org\/abs\/2006.14223"},{"key":"8830_CR34","doi-asserted-by":"crossref","unstructured":"Wang X, Lu Z, Tu Z, Li H, Xiong D, Zhang M. (2017) Neural machine translation advised by statistical machine translation. In: Proceedings of the AAAI conference on artificial intelligence, vol 31, no 1","DOI":"10.1609\/aaai.v31i1.10975"},{"key":"8830_CR35","doi-asserted-by":"crossref","unstructured":"Vaswani A, Zhao Y, Fossum V, Chiang D (2013) Decoding with large-scale neural language models improves translation. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp 1387\u20131392","DOI":"10.18653\/v1\/D13-1140"},{"key":"8830_CR36","unstructured":"Socher R, Huang E, Pennin J, Manning CD, Ng A (2011) Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Advances in neural information processing systems, p 24"},{"key":"8830_CR37","unstructured":"Schwenk H (2012) Continuous space translation models for phrase-based statistical machine translation. In: Proceedings of COLING 2012: posters, pp 1071\u20131080"},{"key":"8830_CR38","doi-asserted-by":"crossref","unstructured":"Ma S, Sun X, Li W, Li S, Li W, Ren X (2018) Query and output: generating words by querying distributed word representations for paraphrase generation. arXiv preprint https:\/\/arxiv.org\/abs\/1803.01465","DOI":"10.18653\/v1\/N18-1018"},{"key":"8830_CR39","doi-asserted-by":"crossref","unstructured":"Siddique AB, Oymak S, Hristidis V (2020) Unsupervised paraphrasing via deep reinforcement learning. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1800\u20131809","DOI":"10.1145\/3394486.3403231"},{"key":"8830_CR40","doi-asserted-by":"crossref","unstructured":"Aghaebrahimian A (2017) Quora question answer dataset. In: Text, speech, and dialogue: 20th international conference, TSD 2017, Prague, Czech Republic, vol 20, pp 66\u201373","DOI":"10.1007\/978-3-319-64206-2_8"},{"key":"8830_CR41","doi-asserted-by":"crossref","unstructured":"Lin T et al (2014) Microsoft coco: common objects in context. In: Computer vision\u2013ECCV 2014: 13th European conference, Zurich, Switzerland, September 6\u201312, 2014, vol 13, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"8830_CR42","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1016\/j.eswa.2018.10.017","volume":"118","author":"S Dabiri","year":"2019","unstructured":"Dabiri S, Heaslip K (2019) Developing a Twitter-based traffic event detection model using deep learning architectures. Expert Syst Appl 118:425\u2013439","journal-title":"Expert Syst Appl"},{"key":"8830_CR43","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"8830_CR44","unstructured":"Niu T, Yavuz S, Zhou Y, Keskar NS, Wang H, Xiong C (2020) Unsupervised paraphrasing with pretrained language models. arXiv preprint https:\/\/arxiv.org\/abs\/2010.12885"},{"key":"8830_CR45","doi-asserted-by":"crossref","unstructured":"Chen W, Tian J, Xiao L, He H, Jin Y (2020) A semantically consistent and syntactically variational encoder\u2013decoder framework for paraphrase generation. In: Proceedings of the 28th international conference on computational linguistics, pp 1186\u20131198","DOI":"10.18653\/v1\/2020.coling-main.102"},{"key":"8830_CR46","doi-asserted-by":"crossref","unstructured":"Guo Z, Huang Z, Zhu KQ, Chen G, Zhang K, Chen B, Huang F (2021) Automatically paraphrasing via sentence reconstruction and round-trip translation. In: IJCAI, pp 3815\u20133821","DOI":"10.24963\/ijcai.2021\/525"},{"key":"8830_CR47","doi-asserted-by":"crossref","unstructured":"Yu J, Cristea AI, Harit A, Sun Z, Aduragba OT, Shi L, Moubayed NA (2023) Deep latent variable models for semi-supervised paraphrase generation. arXiv preprint https:\/\/arxiv.org\/abs\/2301.02275","DOI":"10.2139\/ssrn.4445277"},{"key":"8830_CR48","doi-asserted-by":"crossref","unstructured":"Egonmwan E& Chali Y (2019) Transformer and seq2seq model for paraphrase generation. In: Proceedings of the 3rd workshop on neural generation and translation, pp 249\u2013255","DOI":"10.18653\/v1\/D19-5627"},{"key":"8830_CR49","doi-asserted-by":"crossref","unstructured":"Xie X, Lu X, Chen B (2022) Multi-task learning for paraphrase generation with keyword and part-of-speech reconstruction. In: Findings of the association for computational linguistics: ACL 2022, pp 1234\u20131243","DOI":"10.18653\/v1\/2022.findings-acl.97"}],"updated-by":[{"DOI":"10.1007\/s00521-024-09650-w","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T00:00:00Z","timestamp":1710374400000}}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08830-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08830-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08830-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T17:46:48Z","timestamp":1742665608000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08830-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,17]]},"references-count":49,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["8830"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08830-4","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s00521-024-09650-w","asserted-by":"object"}]},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,17]]},"assertion":[{"value":"5 April 2023","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":"17 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2024","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00521-024-09650-w","URL":"https:\/\/doi.org\/10.1007\/s00521-024-09650-w","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}