{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:14:06Z","timestamp":1750220046797,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T00:00:00Z","timestamp":1676592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,2,17]]},"DOI":"10.1145\/3587716.3587787","type":"proceedings-article","created":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T23:27:30Z","timestamp":1694129250000},"page":"427-432","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Injecting Commonsense Knowledge into Prompt Learning for Zero-Shot Text Classification"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9900-7073","authenticated-orcid":false,"given":"Jing","family":"Qian","sequence":"first","affiliation":[{"name":"School of Advanced Technology, Xi'an Jiaotong-Liverpool University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7430-1645","authenticated-orcid":false,"given":"Qi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7695-4538","authenticated-orcid":false,"given":"Yong","family":"Yue","sequence":"additional","affiliation":[{"name":"School of Advanced Technology, Xi'an Jiaotong-Liverpool University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5683-4106","authenticated-orcid":false,"given":"Katie","family":"Atkinson","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Liverpool, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4006-7472","authenticated-orcid":false,"given":"Gangmin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science &amp; Technology, University of Bedfordshire, UK"}]}],"member":"320","published-online":{"date-parts":[[2023,9,7]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v15i1.18116"},{"key":"e_1_3_2_1_2_1","volume-title":"Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.F. Balcan, and H.\u00a0Lin (Eds.). Vol.\u00a033. Curran Associates","author":"Brown Tom","year":"1877","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared\u00a0D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.F. Balcan, and H.\u00a0Lin (Eds.). Vol.\u00a033. Curran Associates, Inc., 1877\u20131901."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.7763\/IJCTE.2016.V8.1085"},{"key":"e_1_3_2_1_4_1","unstructured":"Ming-Wei Chang Lev-Arie Ratinov Dan Roth and Vivek Srikumar. 2008. Importance of Semantic Representation: Dataless Classification. In AAAI."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3093065"},{"key":"e_1_3_2_1_6_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","volume":"1","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. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171\u20134186."},{"key":"e_1_3_2_1_7_1","volume-title":"Prompt-Learning for Fine-Grained Entity Typing. ArXiv abs\/2108.10604","author":"Ding Ning","year":"2021","unstructured":"Ning Ding, Yulin Chen, Xu Han, Guangwei Xu, Pengjun Xie, Haitao Zheng, Zhiyuan Liu, Juan-Zi Li, and Hong-Gee Kim. 2021. Prompt-Learning for Fine-Grained Entity Typing. ArXiv abs\/2108.10604 (2021)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-demo.10"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/N15-1184"},{"volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)","author":"Gao Tianyu","key":"e_1_3_2_1_10_1","unstructured":"Tianyu Gao, Adam Fisch, and Danqi Chen. 2021. Making Pre-trained Language Models Better Few-shot Learners. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Online, 3816\u20133830."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.576"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00302"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Karen Hambardzumyan Hrant Khachatrian and Jonathan May. 2021. WARP: Word-level Adversarial ReProgramming. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics Online 4921\u20134933.","DOI":"10.18653\/v1\/2021.acl-long.381"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.7763\/IJCTE.2009.V1.32"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.158"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00134"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/612"},{"key":"e_1_3_2_1_18_1","volume-title":"A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Computing Surveys (CSUR)","author":"Liu Pengfei","year":"2022","unstructured":"Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2022. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Computing Surveys (CSUR) (2022)."},{"key":"e_1_3_2_1_19_1","volume-title":"CoRR abs\/2103.10385","author":"Liu Xiao","year":"2021","unstructured":"Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021. GPT Understands, Too. CoRR abs\/2103.10385 (2021). arXiv:2103.10385"},{"key":"e_1_3_2_1_20_1","volume-title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach. ArXiv abs\/1907.11692","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. ArXiv abs\/1907.11692 (2019)."},{"volume-title":"Advances in Neural Information Processing Systems, C.J. Burges, L.\u00a0Bottou, M.\u00a0Welling, Z.\u00a0Ghahramani, and K","author":"Mikolov Tomas","key":"e_1_3_2_1_21_1","unstructured":"Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg\u00a0S Corrado, and Jeff Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Advances in Neural Information Processing Systems, C.J. Burges, L.\u00a0Bottou, M.\u00a0Welling, Z.\u00a0Ghahramani, and K.Q. Weinberger (Eds.). Vol.\u00a026. Curran Associates, Inc."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.7763\/IJCTE.2018.V10.1222"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11431-020-1647-3"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.488"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"volume-title":"Proceedings of the 2021 Conference of the North American","author":"Schick Timo","key":"e_1_3_2_1_27_1","unstructured":"Timo Schick and Hinrich Sch\u00fctze. 2021. It\u2019s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online, 2339\u20132352."},{"key":"e_1_3_2_1_28_1","volume-title":"An Ensemble Method to Produce High-Quality Word Embeddings. ArXiv abs\/1604.01692","author":"Speer Robyn","year":"2016","unstructured":"Robyn Speer and Joshua Chin. 2016. An Ensemble Method to Produce High-Quality Word Embeddings. ArXiv abs\/1604.01692 (2016)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11164"},{"key":"e_1_3_2_1_30_1","volume-title":"Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC\u201912)","author":"Speer Robyn","year":"2012","unstructured":"Robyn Speer and Catherine Havasi. 2012. Representing General Relational Knowledge in ConceptNet 5. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC\u201912). European Language Resources Association (ELRA), Istanbul, Turkey, 3679\u20133686."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.7763\/IJCTE.2022.V14.1322"},{"volume-title":"Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0Von Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). Vol.\u00a030. Curran Associates","author":"Vaswani Ashish","key":"e_1_3_2_1_32_1","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan\u00a0N Gomez, \u0141\u00a0ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems, I.\u00a0Guyon, U.\u00a0Von Luxburg, S.\u00a0Bengio, H.\u00a0Wallach, R.\u00a0Fergus, S.\u00a0Vishwanathan, and R.\u00a0Garnett (Eds.). Vol.\u00a030. Curran Associates, Inc."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-15931-2_19"},{"volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Yin Wenpeng","key":"e_1_3_2_1_34_1","unstructured":"Wenpeng Yin, Jamaal Hay, and Dan Roth. 2019. Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. 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). Association for Computational Linguistics, Hong Kong, China, 3914\u20133923."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N19-1108"},{"key":"e_1_3_2_1_36_1","volume-title":"Proceedings of the 38th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a0139)","author":"Zhao Zihao","year":"2021","unstructured":"Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021. Calibrate Before Use: Improving Few-shot Performance of Language Models. In Proceedings of the 38th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a0139), Marina Meila and Tong Zhang (Eds.). PMLR, 12697\u201312706."}],"event":{"name":"ICMLC 2023: 2023 15th International Conference on Machine Learning and Computing","acronym":"ICMLC 2023","location":"Zhuhai China"},"container-title":["Proceedings of the 2023 15th International Conference on Machine Learning and Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3587716.3587787","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3587716.3587787","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:08:00Z","timestamp":1750183680000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3587716.3587787"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,17]]},"references-count":36,"alternative-id":["10.1145\/3587716.3587787","10.1145\/3587716"],"URL":"https:\/\/doi.org\/10.1145\/3587716.3587787","relation":{},"subject":[],"published":{"date-parts":[[2023,2,17]]},"assertion":[{"value":"2023-09-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}