{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T07:02:06Z","timestamp":1760598126727,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,24]],"date-time":"2022-01-24T00:00:00Z","timestamp":1642982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976103","61872161"],"award-info":[{"award-number":["61976103","61872161"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific and Technological Development Program of Jilin Province","award":["20190302029GX","20180101330JC","20180101328JC"],"award-info":[{"award-number":["20190302029GX","20180101330JC","20180101328JC"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>As a data augmentation method, masking word is commonly used in many natural language processing tasks. However, most mask methods are based on rules and are not related to downstream tasks. In this paper, we propose a novel masking word generator, named Actor-Critic Mask Model (ACMM), which can adaptively adjust the mask strategy according to the performance of downstream tasks. In order to demonstrate the effectiveness of the method, we conducted experiments on two causal event extraction datasets. Experiment results show that, compared with various rule-based masking methods, the masked sentences generated by our proposed method can significantly enhance the generalization of the model and improve the model performance.<\/jats:p>","DOI":"10.3390\/e24020169","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T20:38:58Z","timestamp":1643143138000},"page":"169","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Word-Granular Adversarial Attacks Framework for Causal Event Extraction"],"prefix":"10.3390","volume":"24","author":[{"given":"Yu","family":"Zhao","sequence":"first","affiliation":[{"name":"Colledge of Computer Science and Technology, Jilin University, Changchun 130015, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130015, China"}],"role":[{"role":"author","vocab":"crossref"}]},{"given":"Wanli","family":"Zuo","sequence":"additional","affiliation":[{"name":"Colledge of Computer Science and Technology, Jilin University, Changchun 130015, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130015, China"}],"role":[{"role":"author","vocab":"crossref"}]},{"given":"Shining","family":"Liang","sequence":"additional","affiliation":[{"name":"Colledge of Computer Science and Technology, Jilin University, Changchun 130015, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130015, China"}],"role":[{"role":"author","vocab":"crossref"}]},{"given":"Xiaosong","family":"Yuan","sequence":"additional","affiliation":[{"name":"Colledge of Computer Science and Technology, Jilin University, Changchun 130015, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130015, China"}],"role":[{"role":"author","vocab":"crossref"}]},{"given":"Yijia","family":"Zhang","sequence":"additional","affiliation":[{"name":"Colledge of Computer Science and Technology, Jilin University, Changchun 130015, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130015, China"}],"role":[{"role":"author","vocab":"crossref"}]},{"given":"Xianglin","family":"Zuo","sequence":"additional","affiliation":[{"name":"Colledge of Computer Science and Technology, Jilin University, Changchun 130015, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130015, China"}],"role":[{"role":"author","vocab":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,24]]},"reference":[{"key":"ref_1","first-page":"1083","article-title":"Kernel methods for relation extraction","volume":"3","author":"Dmitry","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_2","unstructured":"Suncong, Z., Feng, W., Hongyun, B., Yuexing, H., Peng, Z., and Bo, X. (August, January 30). Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, BC, Canada."},{"key":"ref_3","unstructured":"Clark, K., Luong, M.-T., Le Quoc, V., and Manning Christopher, D. (2019, January 6\u20139). ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_4","unstructured":"Liu, Y., Myle, O., Naman, G., Jingfei, D., Mandar, J., Danqi, C., Omer, L., Mike, L., Luke, Z., and Veselin, S. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv."},{"key":"ref_5","unstructured":"Devlin, J., Chang, M., Lee, K., and Toutanova, K. (2019, January 6\u20137). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the NAACL-HLT, Minneapolis, MN, USA."},{"key":"ref_6","unstructured":"Kira, R., Sagie, D., and Shaul, M. (2012, January 16\u201320). Learning causality for news events prediction. Proceedings of the 21st International Conference on World Wide Web, New York, NY, USA."},{"key":"ref_7","unstructured":"Chikara, H., Kentaro, T., Julien, K., Motoki, S., Istv\u2019an, V., Jong-Hoon, O., and Yutaka, K. (2014, January 22\u201327). Toward future scenario generation: Extracting event causality exploiting semantic relation, context, and association features. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, MD, USA."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Roxana, G. (2003, January 7\u201312). Automatic detection of causal relations for question answering. Proceedings of the ACL 2003 Workshop on Multilingual Summarization and Question Answering, Sapporo, Japan.","DOI":"10.3115\/1119312.1119322"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-017-0448-y","article-title":"Disease causality extraction based on lexical semantics and document-clause frequency from biomedical literature","volume":"17","author":"Lee","year":"2017","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref_10","unstructured":"Judea, P., and Dana, M. (2018). The Book of Why: The New Science of Cause and Effect, Basic Books."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1093\/llc\/13.4.177","article-title":"Automatic extraction of cause-effect information from newspaper text without knowledge-based inferencing","volume":"13","author":"Khoo","year":"1998","journal-title":"Lit. Linguist. Comput."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gordon, A.S., Bejan, C.A., and Sagae, K. (2011, January 7\u201311). Commonsense causal reasoning using millions of personal stories. Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v25i1.8072"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kruengkrai, C., Torisawa, K., Hashimoto, C., Kloetzer, J., Oh, J., and Tanaka, M. (2017, January 4\u20139). Improving event causality recognition with multiple background knowledge sources using multi-column convolutional neural networks. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11005"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dasgupta, T., Saha, R., Dey, L., and Naskar, A. (2018, January 12\u201314). Automatic extraction of causal relations from text using linguistically informed deep neural networks. Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, Melbourne, Australia.","DOI":"10.18653\/v1\/W18-5035"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.eswa.2018.08.009","article-title":"Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts","volume":"115","author":"Li","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Schomacker, T. (2021). Tropmann-Frick M. Language Representation Models: An Overview. Entropy, 23.","DOI":"10.3390\/e23111422"},{"key":"ref_17","unstructured":"Lan, Z., Chen, M., Sebastian, G., Kevin, G., Piyush, S., and Radu, S. (2019, January 6\u20139). ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_18","unstructured":"Li, D., Nan, Y., Wenhui, W., Furu, W., Xiaodong, L., Yu, W., Jianfeng, G., Ming, Z., and Hsiao-Wuen, H. (2019). Unified language model pre-training for natural language understanding and generation. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1162\/tacl_a_00300","article-title":"Spanbert: Improving pre-training by representing and predicting spans","volume":"8","author":"Mandar","year":"2020","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_20","unstructured":"Wei, W., Bin, B., Ming, Y., Chen, W., Jiangnan, X., Zuyi, B., Liwei, P., and Luo, S. (2019, January 6\u20139). StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chen, S., Jiang, C., Li, J., Xiang, J., and Xiao, W. (2021). Improved Deep Q-Network for User-Side Battery Energy Storage Charging and Discharging Strategy in Industrial Parks. Entropy, 23.","DOI":"10.3390\/e23101311"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xia, K., Feng, J., Yan, C., and Duan, C.B. (2021). BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning. Entropy, 23.","DOI":"10.3390\/e23080932"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wan, K., Wu, D., Zhai, Y., Li, B., Gao, X., and Hu, Z. (2021). An Improved Approach towards Multi-Agent Pursuit\u2013Evasion Game Decision-Making Using Deep Reinforcement Learning. Entropy, 23.","DOI":"10.3390\/e23111433"},{"key":"ref_24","unstructured":"Karthik, N., Adam, Y., and Regina, B. (2016, January 1\u20135). Improving information extraction by acquiring external evidence with reinforcement learning. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP, Austin, TX, USA."},{"key":"ref_25","unstructured":"Hongliang, F., Xu, L., Dingcheng, L., and Ping, L. (August, January 28). End-to-end deep reinforcement learning based coreference resolution. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, T., Huang, M., and Zhao, L. (2018, January 2\u20137). Learning Structured Representation for Text Classification via Reinforcement Learning. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.12047"},{"key":"ref_27","unstructured":"Romain, P., Caiming, X., and Richard, S. (May, January 30). A Deep Reinforced Model for Abstractive Summarization. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_28","unstructured":"Chen, L., Zhang, T., He, D., Ke, G., Wang, L., and Liu, T.-Y. (2020). Variance-reduced language pretraining via a mask proposal network. arXiv."},{"key":"ref_29","unstructured":"Minki, K., Moonsu, H., and Ju, H.S. (2020, January 16\u201320). Neural Mask Generator: Learning to Generate Adaptive Word Maskings for Language Model Adaptation. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hendrickx, I., Kim, S.N., Kozareva, Z., Nakov, P., S\u00e9aghdha, D.\u00d3., Pad\u00f3, S., Pennacchiotti, M., Romano, L., and Szpakowicz, S. (2010, January 15\u201316). SemEval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. Proceedings of the 5th International Workshop on Semantic Evaluation, Los Angeles, CA, USA.","DOI":"10.3115\/1621969.1621986"},{"key":"ref_31","unstructured":"Paramita, M., Rachele, S., Sara, T., and Manuela, S. (2014, January 26\u201327). Annotating causality in the tempeval-3 corpus. Proceedings of the EACL 2014 Workshop on Computational Approaches to Causality in Language (CAtoCL), Gothenburg, Sweden."},{"key":"ref_32","unstructured":"Volodymyr, M., Puigdomenech, B.A., Mehdi, M., Alex, G., Timothy, L., Tim, H., David, S., and Koray, K. (2016, January 19\u201324). Asynchronous methods for deep reinforcement learning. Proceedings of the International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_33","first-page":"1057","article-title":"Policy gradient methods for reinforcement learning with function approximation","volume":"99","author":"Sutton","year":"1999","journal-title":"NIPs"},{"key":"ref_34","unstructured":"Huang, Z., Xu, W., and Yu, K. (2015). Bidirectional LSTM-CRF models for sequence tagging. arXiv."},{"key":"ref_35","unstructured":"Artem, C., Oleksiy, O., Philipp, H., Alexander, B., Matthias, H., Chris, B., and Alexander, P. (2019, January 28\u201331). Targer: Neural argument mining at your fingertips. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Florence, Italy."},{"key":"ref_36","unstructured":"Ilya, L., and Frank, H. (May, January 30). Decoupled Weight Decay Regularization. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_37","unstructured":"Cui, Y., Che, W., Liu, T., Qin, B., and Yang, Z. (2019). Pre-training with whole word masking for chinese bert. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, B., Hou, Y., and Che, W. (2021). Data Augmentation Approaches in Natural Language Processing: A Survey. arXiv.","DOI":"10.1016\/j.aiopen.2022.03.001"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/2\/169\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:06:27Z","timestamp":1760133987000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/2\/169"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,24]]},"references-count":38,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["e24020169"],"URL":"https:\/\/doi.org\/10.3390\/e24020169","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2022,1,24]]}}}