Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial Texts Li, Xinzhe, Liu, Ming, Ma, X and Gao, Longxiang 2021, Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial Texts, in ALTA 2021 : Proceedings of the 19th Workshop of the Australasian Language Technology Association, Australasian Language Technology Association,, pp. 138-148. Attached Files Name Description MIMEType Size Downloads Title Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial Texts Author(s) Li, Xinzhe orcid.org/0000-0002-2160-6111 Liu, Ming Ma, X Gao, Longxiang Conference name Australasian Language Teaching Association. Workshop (19th : 2021 : Online) Conference location Online Conference dates 2021/12/08 - 2021/12/10 Title of proceedings ALTA 2021 : Proceedings of the 19th Workshop of the Australasian Language Technology Association Publication date 2021 Start page 138 End page 148 Total pages 11 Publisher Australasian Language Technology Association Keyword(s) no CORE workshop CORE2020 Australasian B Language eng HERDC Research category E1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30161710 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Checking Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 19 Jan 2022, 09:24:35 EST Wed, 19 Jan 2022, 15:15:57 EST Wed, 19 Jan 2022, 15:18:55 EST Wed, 02 Feb 2022, 13:44:35 EST Wed, 02 Feb 2022, 15:37:16 EST Wed, 20 Apr 2022, 14:43:18 EST Wed, 20 Apr 2022, 16:48:04 EST Wed, 20 Apr 2022, 17:17:31 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 5 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Wed, 02 Feb 2022, 13:44:35 EST