{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T12:58:29Z","timestamp":1784552309745,"version":"3.55.0"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"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":[[2024,5]]},"DOI":"10.1007\/s13042-023-01992-6","type":"journal-article","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T06:02:20Z","timestamp":1699596140000},"page":"1711-1728","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Fine-tuning pretrained transformer encoders for sequence-to-sequence learning"],"prefix":"10.1007","volume":"15","author":[{"given":"Hangbo","family":"Bao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4351-9112","authenticated-orcid":false,"given":"Songhao","family":"Piao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Furu","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"1992_CR1","doi-asserted-by":"crossref","unstructured":"Peters M, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations, in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). New Orleans, Louisiana: Association for Computational Linguistics, June, 2227\u20132237","DOI":"10.18653\/v1\/N18-1202"},{"key":"1992_CR2","unstructured":"Radford A, Narasimhan K, Salimans T, Sutskever I (2018) Improving language understanding by generative pre-training,"},{"key":"1992_CR3","unstructured":"Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I (2019) Language models are unsupervised multitask learners,"},{"key":"1992_CR4","unstructured":"Devlin J, Chang M, Lee K, Toutanova K (2019) 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, Volume 1 (Long and Short Papers), J.\u00a0Burstein, C.\u00a0Doran, and T.\u00a0Solorio, Eds. Association for Computational Linguistics, 4171\u20134186"},{"key":"1992_CR5","unstructured":"Yang Z, Dai Z, Yang Y, Carbonell JG, Salakhutdinov R, Le QV (2019) XLNet: Generalized autoregressive pretraining for language understanding, in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8-14 December 2019, Vancouver, BC, Canada, H.\u00a0M. Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d\u2019Alch\u00e9-Buc, E.\u00a0B. Fox, and R.\u00a0Garnett, Eds., 5754\u20135764"},{"key":"1992_CR6","unstructured":"Dong L, Yang N, Wang W, Wei F, Liu X, Wang Y, Gao J, Zhou M, Hon H (2019) Unified language model pre-training for natural language understanding and generation, in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, 8-14 December 2019, Vancouver, BC, Canada, H.\u00a0M. Wallach, H.\u00a0Larochelle, A.\u00a0Beygelzimer, F.\u00a0d\u2019Alch\u00e9-Buc, E.\u00a0B. Fox, and R.\u00a0Garnett, Eds., 13\u00a0042\u201313\u00a0054"},{"key":"1992_CR7","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: A robustly optimized BERT pretraining approach,\u201d CoRR, vol. abs\/1907.11692,"},{"key":"1992_CR8","doi-asserted-by":"crossref","unstructured":"Conneau A, Khandelwal K, Goyal N, Chaudhary V, Wenzek G, Guzm\u00e1n F, Grave E, Ott M, Zettlemoyer L, Stoyanov V (2020) Unsupervised cross-lingual representation learning at scale, in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, D.\u00a0Jurafsky, J.\u00a0Chai, N.\u00a0Schluter, and J.\u00a0R. Tetreault, Eds. Association for Computational Linguistics, 8440\u20138451","DOI":"10.18653\/v1\/2020.acl-main.747"},{"key":"1992_CR9","unstructured":"Clark K, Luong M-T, Le QV, Manning CD (2020) ELECTRA: Pre-training text encoders as discriminators rather than generators, in ICLR,"},{"key":"1992_CR10","unstructured":"Bao H, Dong L, Wei F, Wang W, Yang N, Liu X, Wang Y, Gao J, Piao S, Zhou M, Hon H (2020) Unilmv2: Pseudo-masked language models for unified language model pre-training, in Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, ser. Proceedings of Machine Learning Research, vol. 119. PMLR, 642\u2013652"},{"issue":"4","key":"1992_CR11","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1177\/107769905303000401","volume":"30","author":"WL Taylor","year":"1953","unstructured":"Taylor WL (1953) Cloze procedure: A new tool for measuring readability. Journalism Bulletin 30(4):415\u2013433","journal-title":"Journalism Bulletin"},{"key":"1992_CR12","unstructured":"Wang A, Cho K (2019) BERT has a mouth, and it must speak: BERT as a markov random field language model,\u201d CoRR, vol. abs\/1902.04094,"},{"key":"1992_CR13","doi-asserted-by":"crossref","unstructured":"Wang A, Singh A, Michael J, Hill F, Levy O, Bowman SR (2019) GLUE: A multi-task benchmark and analysis platform for natural language understanding,\u201d in International Conference on Learning Representations,","DOI":"10.18653\/v1\/W18-5446"},{"key":"1992_CR14","doi-asserted-by":"crossref","unstructured":"Rajpurkar P, Zhang J, Lopyrev K, Liang P (2016) SQuAD: 100,000+ questions for machine comprehension of text, in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Austin, Texas: Association for Computational Linguistics, Nov. 2383\u20132392","DOI":"10.18653\/v1\/D16-1264"},{"key":"1992_CR15","doi-asserted-by":"crossref","unstructured":"Rajpurkar P, Jia R, Liang P (2018) Know what you don\u2019t know: Unanswerable questions for SQuAD, in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 2: Short Papers, 784\u2013789","DOI":"10.18653\/v1\/P18-2124"},{"key":"1992_CR16","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks, in NIPS,"},{"key":"1992_CR17","doi-asserted-by":"crossref","unstructured":"Liu Y, Lapata M (2019) Text summarization with pretrained encoders, 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). Hong Kong, China: Association for Computational Linguistics, Nov. 3728\u20133738","DOI":"10.18653\/v1\/D19-1387"},{"key":"1992_CR18","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1162\/tacl_a_00313","volume":"8","author":"S Rothe","year":"2020","unstructured":"Rothe S, Narayan S, Severyn A (2020) Leveraging pre-trained checkpoints for sequence generation tasks. Trans. Assoc. Comput. Linguistics 8:264\u2013280","journal-title":"Trans. Assoc. Comput. Linguistics"},{"key":"1992_CR19","doi-asserted-by":"crossref","unstructured":"Zou Y, Zhang X, Lu W, Wei F, Zhou M (2020) Pre-training for abstractive document summarization by reinstating source text,\u201d in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Online: Association for Computational Linguistics, Nov. 3646\u20133660. [Online]. Available: https:\/\/aclanthology.org\/2020.emnlp-main.297","DOI":"10.18653\/v1\/2020.emnlp-main.297"},{"key":"1992_CR20","doi-asserted-by":"crossref","unstructured":"Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L (2020) BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,\u201d in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, D.\u00a0Jurafsky, J.\u00a0Chai, N.\u00a0Schluter, and J.\u00a0R. Tetreault, Eds. Association for Computational Linguistics, 7871\u20137880","DOI":"10.18653\/v1\/2020.acl-main.703"},{"issue":"1","key":"1992_CR21","first-page":"5485","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21(1):5485\u20135551","journal-title":"The Journal of Machine Learning Research"},{"key":"1992_CR22","doi-asserted-by":"crossref","unstructured":"Narayan S, Cohen SB, Lapata M (2018) Don\u2019t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization, in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, E.\u00a0Riloff, D.\u00a0Chiang, J.\u00a0Hockenmaier, and J.\u00a0Tsujii, Eds. Association for Computational Linguistics, 1797\u20131807","DOI":"10.18653\/v1\/D18-1206"},{"key":"1992_CR23","unstructured":"Hermann KM, Kocisky T, Grefenstette E, Espeholt L, Kay W, Suleyman M, Blunsom P (2015) Teaching machines to read and comprehend,\u201d in Advances in Neural Information Processing Systems 28, C.\u00a0Cortes, N.\u00a0D. Lawrence, D.\u00a0D. Lee, M.\u00a0Sugiyama, and R.\u00a0Garnett, Eds. Curran Associates, Inc., 1693\u20131701"},{"key":"1992_CR24","unstructured":"Conneau A, Lample G (2019) Cross-lingual language model pretraining, in Advances in Neural Information Processing Systems. Curran Associates, Inc., 7057\u20137067"},{"key":"1992_CR25","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space,\u201d 1st International Conference on Learning Representations, ICLR 2013, Workshop Track Proceedings,"},{"key":"1992_CR26","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation,\u201d in Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"1992_CR27","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need, in Advances in Neural Information Processing Systems 30. Curran Associates, Inc., 5998\u20136008"},{"key":"1992_CR28","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets, in Advances in Neural Information Processing Systems, Z.\u00a0Ghahramani, M.\u00a0Welling, C.\u00a0Cortes, N.\u00a0Lawrence, and K.\u00a0Q. Weinberger, Eds., vol.\u00a027. Curran Associates, Inc.,"},{"key":"1992_CR29","doi-asserted-by":"crossref","unstructured":"Chi Z, Dong L, Wei F, Yang N, Singhal S, Wang W, Song X, Mao X-L, Huang H-Y, Zhou M (2021) Infoxlm: An information-theoretic framework for cross-lingual language model pre-training,\u201d in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 3576\u20133588","DOI":"10.18653\/v1\/2021.naacl-main.280"},{"key":"1992_CR30","doi-asserted-by":"crossref","unstructured":"Zheng B, Che W (2023) Improving cross-lingual language understanding with consistency regularization-based fine-tuning, International Journal of Machine Learning and Cybernetics, 1\u201319,","DOI":"10.1007\/s13042-023-01854-1"},{"key":"1992_CR31","doi-asserted-by":"publisher","first-page":"11 279","DOI":"10.1109\/ACCESS.2020.2965575","volume":"8","author":"Z Li","year":"2020","unstructured":"Li Z, Peng Z, Tang S, Zhang C, Ma H (2020) Text summarization method based on double attention pointer network. IEEE Access 8:11 279-11 288","journal-title":"IEEE Access"},{"key":"1992_CR32","doi-asserted-by":"crossref","unstructured":"Du X, Cardie C (2018) Harvesting paragraph-level question-answer pairs from wikipedia, in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers, I.\u00a0Gurevych and Y.\u00a0Miyao, Eds. Association for Computational Linguistics, 1907\u20131917","DOI":"10.18653\/v1\/P18-1177"},{"key":"1992_CR33","unstructured":"Song K, Tan X, Qin T, Lu J, Liu T (2019) MASS: masked sequence to sequence pre-training for language generation, in Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, ser. Proceedings of Machine Learning Research, K.\u00a0Chaudhuri and R.\u00a0Salakhutdinov, Eds., vol.\u00a097. PMLR, 5926\u20135936"},{"key":"1992_CR34","unstructured":"Liu Y (2019) Fine-tune BERT for extractive summarization, CoRR, vol. abs\/1903.10318,"},{"issue":"9","key":"1992_CR35","doi-asserted-by":"publisher","first-page":"5762","DOI":"10.1109\/TCSVT.2022.3155795","volume":"32","author":"T Xian","year":"2022","unstructured":"Xian T, Li Z, Tang Z, Ma H (2022) Adaptive path selection for dynamic image captioning. IEEE Transactions on Circuits and Systems for Video Technology 32(9):5762\u20135775","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"1992_CR36","unstructured":"Graves A (2013) Generating sequences with recurrent neural networks, CoRR, vol. abs\/1308.0850,"},{"key":"1992_CR37","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1162\/tacl_a_00300","volume":"8","author":"M Joshi","year":"2020","unstructured":"Joshi M, Chen D, Liu Y, Weld DS, Zettlemoyer L, Levy O (2020) Spanbert: Improving pre-training by representing and predicting spans. Transactions of the Association for Computational Linguistics 8:64\u201377","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"1992_CR38","doi-asserted-by":"crossref","unstructured":"Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, Cistac P, Rault T, Louf R, Funtowicz M et al (2020) Transformers: State-of-the-art natural language processing, in Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations, 38\u201345","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"1992_CR39","unstructured":"Lin C-Y (2004) ROUGE: A package for automatic evaluation of summaries, in Text Summarization Branches Out: Proceedings of the ACL-04 Workshop. Barcelona, Spain: Association for Computational Linguistics, Jul. 74\u201381"},{"key":"1992_CR40","doi-asserted-by":"crossref","unstructured":"Papineni K, Roukos S, Ward T, Zhu W-J (2002) BLEU: A method for automatic evaluation of machine translation, in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. Philadelphia, Pennsylvania, USA: Association for Computational Linguistics, Jul. 311\u2013318","DOI":"10.3115\/1073083.1073135"},{"key":"1992_CR41","unstructured":"Banerjee S, Lavie A (2005) 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. Ann Arbor, Michigan: Association for Computational Linguistics, Jun 65\u201372"},{"key":"1992_CR42","doi-asserted-by":"crossref","unstructured":"See A, Liu PJ, Manning CD (2017) Get to the point: Summarization with pointer-generator networks, in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Vancouver, Canada: Association for Computational Linguistics, Jul 1073\u20131083","DOI":"10.18653\/v1\/P17-1099"},{"key":"1992_CR43","doi-asserted-by":"crossref","unstructured":"Zhou Q, Yang N, Wei F, Tan C, Bao H, Zhou M (2017) Neural question generation from text: A preliminary study, in Natural Language Processing and Chinese Computing - 6th CCF International Conference, NLPCC 2017, Dalian, China, November 8-12, 2017, Proceedings, 662\u2013671","DOI":"10.1007\/978-3-319-73618-1_56"},{"key":"1992_CR44","doi-asserted-by":"crossref","unstructured":"Chi Z, Dong L, Wei F, Wang W, Mao X, Huang H (2020) Cross-lingual natural language generation via pre-training, in The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, New York, NY, USA, February 7-12, AAAI Press, 7570\u20137577","DOI":"10.1609\/aaai.v34i05.6256"},{"key":"1992_CR45","doi-asserted-by":"crossref","unstructured":"Sennrich R, Haddow B, Birch A (2016) Neural machine translation of rare words with subword units, in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1715\u20131725","DOI":"10.18653\/v1\/P16-1162"},{"key":"1992_CR46","unstructured":"Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey K, Klingner J, Shah A, Johnson M, Liu X, Kaiser L, Gouws S, Kato Y, Kudo T, Kazawa H, Stevens K, Kurian G, Patil N, Wang W, Young C, Smith J, Riesa J, Rudnick A, Vinyals O, Corrado G, Hughes M, Dean J (2016) Google\u2019s neural machine translation system: Bridging the gap between human and machine translation, CoRR, vol. abs\/1609.08144,"},{"key":"1992_CR47","unstructured":"Kingma DP, Ba J (2015) Adam: A method for stochastic optimization, in 3rd International Conference on Learning Representations, San Diego, CA,"},{"key":"1992_CR48","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"1992_CR49","unstructured":"Paulus R, Xiong C, Socher R (2018) A deep reinforced model for abstractive summarization, CoRR, vol. abs\/1705.04304,"},{"key":"1992_CR50","doi-asserted-by":"crossref","unstructured":"Edunov S, Baevski A, Auli M (2019) Pre-trained language model representations for language generation, CoRR, vol. abs\/1903.09722,","DOI":"10.18653\/v1\/N19-1409"},{"key":"1992_CR51","doi-asserted-by":"crossref","unstructured":"Xiao D, Zhang H, Li Y, Sun Y, Tian H, Wu H, Wang H (2020) ERNIE-GEN: An enhanced multi-flow pre-training and fine-tuning framework for natural language generation, CoRR, vol. abs\/2001.11314,","DOI":"10.24963\/ijcai.2020\/553"},{"key":"1992_CR52","doi-asserted-by":"crossref","unstructured":"Akiyama K, Tamura A, Ninomiya T (2021) Hie-bart: Document summarization with hierarchical bart, in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, 159\u2013165","DOI":"10.18653\/v1\/2021.naacl-srw.20"},{"key":"1992_CR53","doi-asserted-by":"crossref","unstructured":"Du Z, Qian Y, Liu X, Ding M, Qiu J, Yang Z, Tang J (2022) Glm: General language model pretraining with autoregressive blank infilling, in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 320\u2013335","DOI":"10.18653\/v1\/2022.acl-long.26"},{"key":"1992_CR54","unstructured":"Yan Y, Qi W, Gong Y, Liu D, Duan N, Chen J, Zhang R, Zhou M (2020) ProphetNet: Predicting future n-gram for sequence-to-sequence pre-training, CoRR, vol. abs\/2001.04063,"},{"key":"1992_CR55","unstructured":"Zhang J, Zhao Y, Saleh M, Liu PJ (2020) PEGASUS: pre-training with extracted gap-sentences for abstractive summarization, in Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, ser. Proceedings of Machine Learning Research, vol. 119. PMLR, 11 328\u201311 339"},{"key":"1992_CR56","doi-asserted-by":"crossref","unstructured":"Zhao Y, Ni X, Ding Y, Ke Q (2018) \u201cParagraph-level neural question generation with maxout pointer and gated self-attention networks,\u201d in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics, Oct.-Nov. 3901\u20133910","DOI":"10.18653\/v1\/D18-1424"},{"key":"1992_CR57","doi-asserted-by":"crossref","unstructured":"Zhang S, Bansal M (2019) Addressing semantic drift in question generation for semi-supervised question answering, 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, K.\u00a0Inui, J.\u00a0Jiang, V.\u00a0Ng, and X.\u00a0Wan, Eds. Association for Computational Linguistics, 2495\u20132509","DOI":"10.18653\/v1\/D19-1253"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01992-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01992-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01992-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T14:25:15Z","timestamp":1712931915000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01992-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,10]]},"references-count":57,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["1992"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01992-6","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,10]]},"assertion":[{"value":"20 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}