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%0 Conference Proceedings
%T Putting words into the system’s mouth: A targeted attack on neural machine translation using monolingual data poisoning
%A Wang, Jun
%A Xu, Chang
%A Guzmán, Francisco
%A El-Kishky, Ahmed
%A Tang, Yuqing
%A Rubinstein, Benjamin
%A Cohn, Trevor
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F wang-etal-2021-putting-words
%R 10.18653/v1/2021.findings-acl.127
%U https://aclanthology.org/2021.findings-acl.127
%U https://doi.org/10.18653/v1/2021.findings-acl.127
%P 1463-1473

Markdown (Informal)

[Putting words into the system’s mouth: A targeted attack on neural machine translation using monolingual data poisoning](https://aclanthology.org/2021.findings-acl.127) (Wang et al., Findings 2021)

• Putting words into the system’s mouth: A targeted attack on neural machine translation using monolingual data poisoning (Wang et al., Findings 2021)

ACL

• Jun Wang, Chang Xu, Francisco Guzmán, Ahmed El-Kishky, Yuqing Tang, Benjamin Rubinstein, and Trevor Cohn. 2021. Putting words into the system’s mouth: A targeted attack on neural machine translation using monolingual data poisoning. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 1463–1473, Online. Association for Computational Linguistics.