{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:16:05Z","timestamp":1750220165525,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":16,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T00:00:00Z","timestamp":1659571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,4]]},"DOI":"10.1145\/3556384.3556386","type":"proceedings-article","created":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T10:51:35Z","timestamp":1667040695000},"page":"8-12","source":"Crossref","is-referenced-by-count":0,"title":["Performance Comparison of Seven Pretrained Models on a text classification task"],"prefix":"10.1145","author":[{"given":"Enhao","family":"Tan","sequence":"first","affiliation":[{"name":"School of Computer Sciencec (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haowei","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Southern California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,29]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"1","volume":"1","author":"Minaee S","unstructured":"Minaee S , Kalchbrenner N , Cambria E , Nikzad N , Chenaghlu M , Gao J. Deep learning\u2013based text classification: a comprehensive review. ACM Computing Surveys (CSUR). 2021 Apr 1 7;54(3): 1 - 40 . Minaee S, Kalchbrenner N, Cambria E, Nikzad N, Chenaghlu M, Gao J. Deep learning\u2013based text classification: a comprehensive review. ACM Computing Surveys (CSUR). 2021 Apr 17;54(3):1-40.","journal-title":"Apr"},{"doi-asserted-by":"crossref","unstructured":"Wang A Singh A Michael J Hill F Levy O Bowman SR. GLUE: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461. 2018 Apr 20.  Wang A Singh A Michael J Hill F Levy O Bowman SR. GLUE: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461. 2018 Apr 20.","key":"e_1_3_2_1_2_1","DOI":"10.18653\/v1\/W18-5446"},{"doi-asserted-by":"crossref","unstructured":"Liu X He P Chen W Gao J. Multi-task deep neural networks for natural language understanding. arXiv preprint arXiv:1901.11504. 2019 Jan 31.  Liu X He P Chen W Gao J. Multi-task deep neural networks for natural language understanding. arXiv preprint arXiv:1901.11504. 2019 Jan 31.","key":"e_1_3_2_1_3_1","DOI":"10.18653\/v1\/P19-1441"},{"volume-title":"InProceedings of the 2013 conference on empirical methods in natural language processing 2013 Oct (pp. 1631-1642)","author":"Socher R","unstructured":"Socher R , Perelygin A , Wu J , Chuang J , Manning CD , Ng AY , Potts C. Recursive deep models for semantic compositionality over a sentiment treebank . InProceedings of the 2013 conference on empirical methods in natural language processing 2013 Oct (pp. 1631-1642) . Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng AY, Potts C. Recursive deep models for semantic compositionality over a sentiment treebank. InProceedings of the 2013 conference on empirical methods in natural language processing 2013 Oct (pp. 1631-1642).","key":"e_1_3_2_1_4_1"},{"doi-asserted-by":"crossref","unstructured":"Iyyer M Manjunatha V Boyd-Graber J Daum\u00e9 III H. Deep unordered composition rivals syntactic methods for text classification. InProceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: Long papers) 2015 Jul (pp. 1681-1691).  Iyyer M Manjunatha V Boyd-Graber J Daum\u00e9 III H. Deep unordered composition rivals syntactic methods for text classification. InProceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: Long papers) 2015 Jul (pp. 1681-1691).","key":"e_1_3_2_1_5_1","DOI":"10.3115\/v1\/P15-1162"},{"unstructured":"Mikolov T Sutskever I Chen K Corrado GS Dean J. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems. 2013;26.  Mikolov T Sutskever I Chen K Corrado GS Dean J. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems. 2013;26.","key":"e_1_3_2_1_6_1"},{"unstructured":"Vaswani A Shazeer N Parmar N Uszkoreit J Jones L Gomez AN Kaiser \u0141 Polosukhin I. Attention is all you need. Advances in neural information processing systems. 2017;30.  Vaswani A Shazeer N Parmar N Uszkoreit J Jones L Gomez AN Kaiser \u0141 Polosukhin I. Attention is all you need. Advances in neural information processing systems. 2017;30.","key":"e_1_3_2_1_7_1"},{"unstructured":"Radford A Narasimhan K Salimans T Sutskever I. Improving language understanding by generative pre-training.  Radford A Narasimhan K Salimans T Sutskever I. Improving language understanding by generative pre-training.","key":"e_1_3_2_1_8_1"},{"unstructured":"Devlin J Chang MW Lee K Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. 2018 Oct 11.  Devlin J Chang MW Lee K Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. 2018 Oct 11.","key":"e_1_3_2_1_9_1"},{"unstructured":"Liu Y Ott M Goyal N Du J Joshi M Chen D Levy O Lewis M Zettlemoyer L Stoyanov V. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. 2019 Jul 26.  Liu Y Ott M Goyal N Du J Joshi M Chen D Levy O Lewis M Zettlemoyer L Stoyanov V. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. 2019 Jul 26.","key":"e_1_3_2_1_10_1"},{"unstructured":"Lan Z Chen M Goodman S Gimpel K Sharma P Soricut R. Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942. 2019 Sep 26.  Lan Z Chen M Goodman S Gimpel K Sharma P Soricut R. Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942. 2019 Sep 26.","key":"e_1_3_2_1_11_1"},{"unstructured":"Sanh V Debut L Chaumond J Wolf T. DistilBERT a distilled version of BERT: smaller faster cheaper and lighter. arXiv preprint arXiv:1910.01108. 2019 Oct 2.  Sanh V Debut L Chaumond J Wolf T. DistilBERT a distilled version of BERT: smaller faster cheaper and lighter. arXiv preprint arXiv:1910.01108. 2019 Oct 2.","key":"e_1_3_2_1_12_1"},{"unstructured":"Clark K Luong MT Le QV Manning CD. Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555. 2020 Mar 23.  Clark K Luong MT Le QV Manning CD. Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555. 2020 Mar 23.","key":"e_1_3_2_1_13_1"},{"volume-title":"Learning word vectors for sentiment analysis. InProceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies 2011 Jun (pp. 142-150)","author":"Maas A","unstructured":"Maas A , Daly RE , Pham PT , Huang D , Ng AY , Potts C. Learning word vectors for sentiment analysis. InProceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies 2011 Jun (pp. 142-150) . Maas A, Daly RE, Pham PT, Huang D, Ng AY, Potts C. Learning word vectors for sentiment analysis. InProceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies 2011 Jun (pp. 142-150).","key":"e_1_3_2_1_14_1"},{"key":"e_1_3_2_1_15_1","first-page":"12837","volume":"202","author":"Jiang ZH","unstructured":"Jiang ZH , Yu W , Zhou D , Chen Y , Feng J , Yan S. Convbert : Improving bert with span-based dynamic convolution. Advances in Neural Information Processing Systems. 202 0;33: 12837 - 12848 . Jiang ZH, Yu W, Zhou D, Chen Y, Feng J, Yan S. Convbert: Improving bert with span-based dynamic convolution. Advances in Neural Information Processing Systems. 2020;33:12837-48.","journal-title":"Neural Information Processing Systems."},{"unstructured":"Yang Z Dai Z Yang Y Carbonell J Salakhutdinov RR Le QV. Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems. 2019;32.  Yang Z Dai Z Yang Y Carbonell J Salakhutdinov RR Le QV. Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems. 2019;32.","key":"e_1_3_2_1_16_1"}],"event":{"acronym":"SPML 2022","name":"SPML 2022: 2022 5th International Conference on Signal Processing and Machine Learning","location":"Dalian China"},"container-title":["2022 5th International Conference on Signal Processing and Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3556384.3556386","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3556384.3556386","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:32Z","timestamp":1750186832000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3556384.3556386"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,4]]},"references-count":16,"alternative-id":["10.1145\/3556384.3556386","10.1145\/3556384"],"URL":"https:\/\/doi.org\/10.1145\/3556384.3556386","relation":{},"subject":[],"published":{"date-parts":[[2022,8,4]]}}}