{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:43:29Z","timestamp":1772909009279,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:00:00Z","timestamp":1675036800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:00:00Z","timestamp":1675036800000},"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":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s13042-023-01784-y","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T14:32:18Z","timestamp":1675089138000},"page":"2591-2605","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Data augmentation using Heuristic Masked Language Modeling"],"prefix":"10.1007","volume":"14","author":[{"given":"Xiaorong","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yuan","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Ping","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,30]]},"reference":[{"key":"1784_CR1","unstructured":"Xie Q, Dai Z, Hovy E.H, Luong T, Le Q (2020) Unsupervised data augmentation for consistency training. In: Advances in neural information processing systems 33: annual conference on neural information processing systems 2020, NeurIPS 2020, December 6-12,"},{"key":"1784_CR2","doi-asserted-by":"crossref","unstructured":"Anaby-Tavor A, Carmeli B, Goldbraich E, Kantor A, Kour G, Shlomov S, Tepper N, Zwerdling N (2020) Do not have enough data? deep learning to the rescue! In: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, New York, NY, USA, February 7-12, 2020, pp. 7383\u20137390","DOI":"10.1609\/aaai.v34i05.6233"},{"key":"1784_CR3","doi-asserted-by":"crossref","unstructured":"Wang J, Yang Y, Liu K, Xie P, Liu X (2022) Instance-guided multi-modal fake news detection with dynamic intra- and inter-modality fusion. In: Advances in knowledge discovery and data mining\u201426th Pacific-Asia conference, PAKDD 2022, Chengdu, China, May 16-19, 2022, pp. 510\u2013521","DOI":"10.1007\/978-3-031-05933-9_40"},{"key":"1784_CR4","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.ins.2021.11.034","volume":"583","author":"K Liu","year":"2022","unstructured":"Liu K, Li T, Yang X, Yang X, Liu D, Zhang P (2022) Wang J Granular cabin: an efficient solution to neighborhood learning in big data. Inform Sci 583:189\u2013201","journal-title":"Inform Sci"},{"key":"1784_CR5","doi-asserted-by":"crossref","unstructured":"Tobin J, Fong R, Ray A, Schneider J, Zaremba W, Abbeel P (2017) Domain randomization for transferring deep neural networks from simulation to the real world. In: 2017 IEEE\/RSJ International conference on intelligent robots and systems, IROS 2017, Vancouver, BC, Canada, September 24-28, 2017, pp. 23\u201330","DOI":"10.1109\/IROS.2017.8202133"},{"key":"1784_CR6","doi-asserted-by":"crossref","unstructured":"Hoang C.D.V, Koehn P, Haffari G, Cohn T (2018) Iterative back-translation for neural machine translation. In: Proceedings of the 2nd workshop on neural machine translation and generation, NMT@ACL 2018, Melbourne, Australia, July 20, 2018, pp. 18\u201324","DOI":"10.18653\/v1\/W18-2703"},{"key":"1784_CR7","doi-asserted-by":"crossref","unstructured":"Edunov S, Ott M, Auli M, Grangier D (2018) Understanding back-translation at scale. In: Proceedings of the 2018 conference on empirical methods in natural language processing, Brussels, Belgium, October 31 - November 4, 2018, pp. 489\u2013500","DOI":"10.18653\/v1\/D18-1045"},{"key":"1784_CR8","doi-asserted-by":"crossref","unstructured":"Fadaee M, Bisazza A, Monz C (2017) Data augmentation for low-resource neural machine translation. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, pp. 567\u2013573","DOI":"10.18653\/v1\/P17-2090"},{"key":"1784_CR9","doi-asserted-by":"crossref","unstructured":"Kobayashi S (2018) Contextual augmentation: Data augmentation by words with paradigmatic relations. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT, New Orleans, Louisiana, USA, June 1-6, 2018, pp. 452\u2013457","DOI":"10.18653\/v1\/N18-2072"},{"key":"1784_CR10","doi-asserted-by":"crossref","unstructured":"Wu X, Lv S, Zang L, Han J, Hu S (2019) Conditional bert contextual augmentation. In: Computational Science\u2014ICCS 2019\u201419th International Conference, Faro, Portugal, June 12-14, 2019, pp. 84\u201395","DOI":"10.1007\/978-3-030-22747-0_7"},{"key":"1784_CR11","doi-asserted-by":"crossref","unstructured":"Liu T, Cui Y, Yin Q, Zhang W, Wang S, Hu G (2017) Generating and exploiting large-scale pseudo training data for zero pronoun resolution. In: Proceedings of the 55th annual meeting of the association for computational linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, pp. 102\u2013111","DOI":"10.18653\/v1\/P17-1010"},{"key":"1784_CR12","unstructured":"Hou Y, Liu Y, Che W, Liu T (2018) Sequence-to-sequence data augmentation for dialogue language understanding. In: Proceedings of the 27th international conference on computational linguistics, COLING 2018, Santa Fe, New Mexico, USA, August 20-26, 2018, pp. 1234\u20131245"},{"key":"1784_CR13","doi-asserted-by":"crossref","unstructured":"Dong L, Mallinson J, Reddy S, Lapata M (2017) Learning to paraphrase for question answering. In: Proceedings of the 2017 conference on empirical methods in natural language processing, EMNLP 2017, Copenhagen, Denmark, September 9-11, 2017, pp. 875\u2013886","DOI":"10.18653\/v1\/D17-1091"},{"key":"1784_CR14","doi-asserted-by":"crossref","unstructured":"Wei JW, Zou K (2019) EDA: Easy data augmentation techniques for boosting performance on text classification tasks. 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 2019, Hong Kong, China, November 3-7, 2019, pp. 6382\u20136388","DOI":"10.18653\/v1\/D19-1670"},{"key":"1784_CR15","doi-asserted-by":"crossref","unstructured":"Dai X, Adel H (2020) An analysis of simple data augmentation for named entity recognition. In: Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020, pp. 3861\u20133867","DOI":"10.18653\/v1\/2020.coling-main.343"},{"key":"1784_CR16","doi-asserted-by":"crossref","unstructured":"Vania C, Kementchedjhieva Y, S\u00f8gaard A, Lopez A (2019) A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, pp. 1105\u20131116","DOI":"10.18653\/v1\/D19-1102"},{"key":"1784_CR17","doi-asserted-by":"crossref","unstructured":"Gulordava K, Bojanowski P, Grave E, Linzen T, Baroni M Colorless green recurrent networks dream hierarchically. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1-6, 2018, pp. 1195\u20131205","DOI":"10.18653\/v1\/N18-1108"},{"key":"1784_CR18","doi-asserted-by":"crossref","unstructured":"Sennrich R, Haddow B, Birch A Edinburgh neural machine translation systems for WMT 16. In: Proceedings of the first conference on machine translation, WMT 2016, colocated with ACL 2016, August 11-12, Berlin, Germany, pp. 371\u2013376","DOI":"10.18653\/v1\/W16-2323"},{"key":"1784_CR19","unstructured":"Gal Y, Ghahramani Z A theoretically grounded application of dropout in recurrent neural networks. In: Advances in neural information processing systems 29: annual conference on neural information processing systems 2016, December 5-10, 2016, pp. 1019\u20131027"},{"key":"1784_CR20","unstructured":"Norouzi M, Bengio S, Chen Z, Jaitly N, Schuster M, Wu Y, Schuurmans D Reward augmented maximum likelihood for neural structured prediction. In: Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pp. 1723\u20131731"},{"key":"1784_CR21","doi-asserted-by":"crossref","unstructured":"Sennrich R, Haddow B, Birch A Improving neural machine translation models with monolingual data. In: Proceedings of the 54th annual meeting of the association for computational linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, pp. 86\u201396","DOI":"10.18653\/v1\/P16-1009"},{"key":"1784_CR22","doi-asserted-by":"crossref","unstructured":"Mallinson J, Sennrich R, Lapata M Paraphrasing revisited with neural machine translation. In: Proceedings of the 15th conference of the European chapter of the association for computational linguistics, EACL 2017, Valencia, Spain, April 3-7, 2017, pp. 881\u2013893","DOI":"10.18653\/v1\/E17-1083"},{"key":"1784_CR23","unstructured":"Yu A.W, Dohan D, Luong M, Zhao R, Chen K, Norouzi M, Le Q.V Qanet: Combining local convolution with global self-attention for reading comprehension. In: 6th international conference on learning representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018"},{"key":"1784_CR24","doi-asserted-by":"crossref","unstructured":"Li Y, Cohn T, Baldwin T Robust training under linguistic adversity. In: Proceedings of the 15th Conference of the European chapter of the association for computational linguistics, EACL 2017, Valencia, Spain, April 3-7, 2017, pp. 21\u201327","DOI":"10.18653\/v1\/E17-2004"},{"key":"1784_CR25","doi-asserted-by":"crossref","unstructured":"Yasunaga M, Kasai J, Radev D.R Robust multilingual part-of-speech tagging via adversarial training. In: Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1-6, 2018, pp. 976\u2013986","DOI":"10.18653\/v1\/N18-1089"},{"key":"1784_CR26","doi-asserted-by":"crossref","unstructured":"Alzantot M, Sharma Y, Elgohary A, Ho B, Srivastava M.B, Chang K Generating natural language adversarial examples. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, pp. 2890\u20132896","DOI":"10.18653\/v1\/D18-1316"},{"key":"1784_CR27","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1007\/s11431-020-1647-3","volume":"63","author":"X Qiu","year":"2020","unstructured":"Qiu X, Sun T, Xu Y, Shao Y, Dai N (2020) Huang X Pre-trained models for natural language processing: a survey. Sci China Technol Sci 63:1872\u20131897","journal-title":"Sci China Technol Sci"},{"key":"1784_CR28","unstructured":"Devlin J, Chang M, Lee K, Toutanova K BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, pp. 4171\u20134186"},{"key":"1784_CR29","doi-asserted-by":"crossref","unstructured":"Sun WSLYFSTHWHWH Y Ernie 2.0: A continual pre-training framework for language understanding. In: The Thirty-fourth AAAI conference on artificial intelligence, AAAI 2020, New York, NY, USA, February 7-12, 2020, pp. 8968\u20138975","DOI":"10.1609\/aaai.v34i05.6428"},{"key":"1784_CR30","doi-asserted-by":"crossref","unstructured":"Cui Y, Che W, Liu T, Qin B, Yang Z Pre-training with whole word masking for chinese BERT. IEEE ACM Trans. Audio Speech Lang. Process. 29 3504\u20133514 (2021)","DOI":"10.1109\/TASLP.2021.3124365"},{"key":"1784_CR31","doi-asserted-by":"crossref","unstructured":"Xie Z, Huang Y, Zhu Y, Jin L, Liu Y, Xie L Aggregation cross-entropy for sequence recognition. In: IEEE conference on computer vision and pattern recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pp. 6538\u20136547","DOI":"10.1109\/CVPR.2019.00670"},{"key":"1784_CR32","doi-asserted-by":"crossref","unstructured":"Taylor W.L \u201ccloze procedure\u201d: A new tool for measuring readability. Journalism quarterly 30(4), 415\u2013433 (1953)","DOI":"10.1177\/107769905303000401"},{"key":"1784_CR33","doi-asserted-by":"publisher","first-page":"185476","DOI":"10.1109\/ACCESS.2019.2960263","volume":"7","author":"S Yu","year":"2019","unstructured":"Yu S, Yang J, Liu D, Li R, Zhang Y (2019) Zhao S Hierarchical data augmentation and the application in text classification. IEEE Access 7:185476\u2013185485","journal-title":"IEEE Access"},{"key":"1784_CR34","doi-asserted-by":"crossref","unstructured":"Thakur N, Reimers N, Daxenberger J, Gurevych I Augmented SBERT: data augmentation method for improving bi-encoders for pairwise sentence scoring tasks. In: Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, pp. 296\u2013310","DOI":"10.18653\/v1\/2021.naacl-main.28"},{"key":"1784_CR35","doi-asserted-by":"crossref","unstructured":"Kim Y Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing, EMNLP 2014, October 25-29, 2014, pp. 1746\u20131751","DOI":"10.3115\/v1\/D14-1181"},{"key":"1784_CR36","unstructured":"Mihalcea R, Tarau P Textrank: Bringing order into texts. In: Proceedings of the 2016 conference on empirical methods in natural language processing, EMNLP 2004,Barcelona, Spain, July"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01784-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01784-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01784-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T08:23:23Z","timestamp":1686990203000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01784-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,30]]},"references-count":36,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["1784"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01784-y","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,30]]},"assertion":[{"value":"29 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}