{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:19:09Z","timestamp":1766578749982,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen Science and Technology Innovation Program","award":["KQTD20190929172835662"],"award-info":[{"award-number":["KQTD20190929172835662"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906185"],"award-info":[{"award-number":["61906185"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Youth Innovation Promotion Association of CAS China","award":["2020357"],"award-info":[{"award-number":["2020357"]}]},{"name":"Natural Science Foundation of Guangdong Province of China","award":["No. 2019A1515011705 and No. 2021A1515011905"],"award-info":[{"award-number":["No. 2019A1515011705 and No. 2021A1515011905"]}]},{"name":"Shenzhen Basic Research Foundation","award":["No. JCYJ20210324115614039 and No. JCYJ20200109113441941"],"award-info":[{"award-number":["No. JCYJ20210324115614039 and No. JCYJ20200109113441941"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,7,6]]},"DOI":"10.1145\/3477495.3531887","type":"proceedings-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T15:12:13Z","timestamp":1657206733000},"page":"2513-2518","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Dual Pseudo Supervision for Semi-Supervised Text Classification with a Reliable Teacher"],"prefix":"10.1145","author":[{"given":"Shujie","family":"Li","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Min","family":"Yang","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}]},{"given":"Chengming","family":"Li","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Shenzhen, China"}]},{"given":"Ruifeng","family":"Xu","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology &amp; Peng Cheng Lab, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"volume-title":"Neural networks: Tricks of the trade","author":"Bottou L\u00e9on","key":"e_1_3_2_2_2_1","unstructured":"L\u00e9on Bottou . 2012. Stochastic gradient descent tricks . In Neural networks: Tricks of the trade . Springer , 421--436. L\u00e9on Bottou. 2012. Stochastic gradient descent tricks. In Neural networks: Tricks of the trade. Springer, 421--436."},{"key":"e_1_3_2_2_3_1","first-page":"830","article-title":"Importance of Semantic Representation: Dataless Classification","volume":"2","author":"Chang Ming-Wei","year":"2008","unstructured":"Ming-Wei Chang , Lev-Arie Ratinov , Dan Roth , and Vivek Srikumar . 2008 . Importance of Semantic Representation: Dataless Classification .. In AAAI , Vol. 2. 830 -- 835 . Ming-Wei Chang, Lev-Arie Ratinov, Dan Roth, and Vivek Srikumar. 2008. Importance of Semantic Representation: Dataless Classification.. In AAAI, Vol. 2. 830--835.","journal-title":"AAAI"},{"key":"e_1_3_2_2_4_1","volume-title":"Mixtext: Linguistically-informed interpolation of hidden space for semi-supervised text classification. ACL","author":"Chen Jiaao","year":"2020","unstructured":"Jiaao Chen , Zichao Yang , and Diyi Yang . 2020 . Mixtext: Linguistically-informed interpolation of hidden space for semi-supervised text classification. ACL (2020). Jiaao Chen, Zichao Yang, and Diyi Yang. 2020. Mixtext: Linguistically-informed interpolation of hidden space for semi-supervised text classification. ACL (2020)."},{"key":"e_1_3_2_2_5_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. NAACL","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2019 . Bert: Pre-training of deep bidirectional transformers for language understanding. NAACL (2019). Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. NAACL (2019)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCSE.2013.6553926"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Xin Luna Dong and Gerard de Melo. 2019. A robust self-learning framework for cross-lingual text classification. In EMNLP-IJCNLP. 6306--6310.  Xin Luna Dong and Gerard de Melo. 2019. A robust self-learning framework for cross-lingual text classification. In EMNLP-IJCNLP. 6306--6310.","DOI":"10.18653\/v1\/D19-1658"},{"key":"e_1_3_2_2_8_1","volume-title":"International Conference on Machine Learning. PMLR, 1126--1135","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn , Pieter Abbeel , and Sergey Levine . 2017 . Model-agnostic metalearning for fast adaptation of deep networks . In International Conference on Machine Learning. PMLR, 1126--1135 . Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic metalearning for fast adaptation of deep networks. In International Conference on Machine Learning. PMLR, 1126--1135."},{"key":"e_1_3_2_2_9_1","volume-title":"Variational pretraining for semi-supervised text classification. arXiv preprint arXiv:1906.02242","author":"Gururangan Suchin","year":"2019","unstructured":"Suchin Gururangan , Tam Dang , Dallas Card , and Noah A Smith . 2019. Variational pretraining for semi-supervised text classification. arXiv preprint arXiv:1906.02242 ( 2019 ). Suchin Gururangan, Tam Dang, Dallas Card, and Noah A Smith. 2019. Variational pretraining for semi-supervised text classification. arXiv preprint arXiv:1906.02242 (2019)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526773"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441814"},{"key":"e_1_3_2_2_12_1","volume-title":"Workshop on challenges in representation learning, ICML","volume":"3","author":"Dong-Hyun","unstructured":"Dong-Hyun Lee et al. 2013. Pseudo-label: The simple and efficient semisupervised learning method for deep neural networks . In Workshop on challenges in representation learning, ICML , Vol. 3 . 896. Dong-Hyun Lee et al. 2013. Pseudo-label: The simple and efficient semisupervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML, Vol. 3. 896."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.391"},{"key":"e_1_3_2_2_14_1","volume-title":"Adversarial training methods for semi-supervised text classification. arXiv preprint arXiv:1605.07725","author":"Miyato Takeru","year":"2016","unstructured":"Takeru Miyato , Andrew M Dai , and Ian Goodfellow . 2016. Adversarial training methods for semi-supervised text classification. arXiv preprint arXiv:1605.07725 ( 2016 ). Takeru Miyato, Andrew M Dai, and Ian Goodfellow. 2016. Adversarial training methods for semi-supervised text classification. arXiv preprint arXiv:1605.07725 (2016)."},{"key":"e_1_3_2_2_15_1","volume-title":"Virtual adversarial training: a regularization method for supervised and semi-supervised learning","author":"Miyato Takeru","year":"2018","unstructured":"Takeru Miyato , Shin-ichi Maeda, Masanori Koyama , and Shin Ishii . 2018. Virtual adversarial training: a regularization method for supervised and semi-supervised learning . IEEE transactions on pattern analysis and machine intelligence 41, 8 ( 2018 ), 1979--1993. Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. 2018. Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE transactions on pattern analysis and machine intelligence 41, 8 (2018), 1979--1993."},{"key":"e_1_3_2_2_16_1","volume-title":"Uncertainty-aware selftraining for few-shot text classification. Advances in Neural Information Processing Systems 33","author":"Mukherjee Subhabrata","year":"2020","unstructured":"Subhabrata Mukherjee and Ahmed Awadallah . 2020. Uncertainty-aware selftraining for few-shot text classification. Advances in Neural Information Processing Systems 33 ( 2020 ). Subhabrata Mukherjee and Ahmed Awadallah. 2020. Uncertainty-aware selftraining for few-shot text classification. Advances in Neural Information Processing Systems 33 (2020)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"Kamal Nigam Andrew McCallum and Tom M Mitchell. 2006. Semi-Supervised Text Classification Using EM.  Kamal Nigam Andrew McCallum and Tom M Mitchell. 2006. Semi-Supervised Text Classification Using EM.","DOI":"10.7551\/mitpress\/9780262033589.003.0003"},{"key":"e_1_3_2_2_18_1","volume-title":"Thumbs up? Sentiment classification using machine learning techniques. EMNLP","author":"Pang Bo","year":"2002","unstructured":"Bo Pang , Lillian Lee , and Shivakumar Vaithyanathan . 2002. Thumbs up? Sentiment classification using machine learning techniques. EMNLP ( 2002 ), 79--86. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up? Sentiment classification using machine learning techniques. EMNLP (2002), 79--86."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.03.020"},{"key":"e_1_3_2_2_20_1","volume-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. NIPS","author":"Tarvainen Antti","year":"2017","unstructured":"Antti Tarvainen and Harri Valpola . 2017. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. NIPS ( 2017 ). Antti Tarvainen and Harri Valpola. 2017. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. NIPS (2017)."},{"key":"e_1_3_2_2_21_1","volume-title":"The 34th Conference on Neural Information Processing Systems","author":"Xie Qizhe","year":"2019","unstructured":"Qizhe Xie , Zihang Dai , Eduard Hovy , Minh-Thang Luong , and Quoc V Le . 2019 . Unsupervised data augmentation for consistency training . The 34th Conference on Neural Information Processing Systems (2019). Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, and Quoc V Le. 2019. Unsupervised data augmentation for consistency training. The 34th Conference on Neural Information Processing Systems (2019)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298023.3298056"},{"key":"e_1_3_2_2_23_1","volume-title":"Character-level convolutional networks for text classification. Advances in neural information processing systems 28","author":"Zhang Xiang","year":"2015","unstructured":"Xiang Zhang , Junbo Zhao , and Yann LeCun . 2015. Character-level convolutional networks for text classification. Advances in neural information processing systems 28 ( 2015 ), 649--657. Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text classification. Advances in neural information processing systems 28 (2015), 649--657."},{"volume-title":"Enhanced semi-supervised learning for multimodal emotion recognition","author":"Zhang Zixing","key":"e_1_3_2_2_24_1","unstructured":"Zixing Zhang , Fabien Ringeval , Bin Dong , Eduardo Coutinho , Erik Marchi , and Bjorn Schuller . 2016. Enhanced semi-supervised learning for multimodal emotion recognition . In ICASSP. IEEE. Zixing Zhang, Fabien Ringeval, Bin Dong, Eduardo Coutinho, Erik Marchi, and Bjorn Schuller. 2016. Enhanced semi-supervised learning for multimodal emotion recognition. In ICASSP. IEEE."}],"event":{"name":"SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Madrid Spain","acronym":"SIGIR '22"},"container-title":["Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477495.3531887","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3477495.3531887","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:10:27Z","timestamp":1750183827000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477495.3531887"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,6]]},"references-count":24,"alternative-id":["10.1145\/3477495.3531887","10.1145\/3477495"],"URL":"https:\/\/doi.org\/10.1145\/3477495.3531887","relation":{},"subject":[],"published":{"date-parts":[[2022,7,6]]},"assertion":[{"value":"2022-07-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}