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    <abstract>Neural machine translation systems tend to fail on less decent inputs despite its significant efficacy, which may significantly harm the credibility of these systems—fathoming how and when neural-based systems fail in such cases is critical for industrial maintenance. Instead of collecting and analyzing bad cases using limited handcrafted error features, here we investigate this issue by generating adversarial examples via a new paradigm based on reinforcement learning. Our paradigm could expose pitfalls for a given performance metric, e.g., BLEU, and could target any given neural machine translation architecture. We conduct experiments of adversarial attacks on two mainstream neural machine translation architectures, RNN-search, and Transformer. The results show that our method efficiently produces stable attacks with meaning-preserving adversarial examples. We also present a qualitative and quantitative analysis for the preference pattern of the attack, demonstrating its capability of pitfall exposure.</abstract>
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%0 Conference Proceedings
%T A Reinforced Generation of Adversarial Examples for Neural Machine Translation
%A Zou, Wei
%A Huang, Shujian
%A Xie, Jun
%A Dai, Xinyu
%A Chen, Jiajun
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zou-etal-2020-reinforced
%X Neural machine translation systems tend to fail on less decent inputs despite its significant efficacy, which may significantly harm the credibility of these systems—fathoming how and when neural-based systems fail in such cases is critical for industrial maintenance. Instead of collecting and analyzing bad cases using limited handcrafted error features, here we investigate this issue by generating adversarial examples via a new paradigm based on reinforcement learning. Our paradigm could expose pitfalls for a given performance metric, e.g., BLEU, and could target any given neural machine translation architecture. We conduct experiments of adversarial attacks on two mainstream neural machine translation architectures, RNN-search, and Transformer. The results show that our method efficiently produces stable attacks with meaning-preserving adversarial examples. We also present a qualitative and quantitative analysis for the preference pattern of the attack, demonstrating its capability of pitfall exposure.
%R 10.18653/v1/2020.acl-main.319
%U https://aclanthology.org/2020.acl-main.319
%U https://doi.org/10.18653/v1/2020.acl-main.319
%P 3486-3497

Markdown (Informal)

[A Reinforced Generation of Adversarial Examples for Neural Machine Translation](https://aclanthology.org/2020.acl-main.319) (Zou et al., ACL 2020)

• A Reinforced Generation of Adversarial Examples for Neural Machine Translation (Zou et al., ACL 2020)

ACL

• Wei Zou, Shujian Huang, Jun Xie, Xinyu Dai, and Jiajun Chen. 2020. A Reinforced Generation of Adversarial Examples for Neural Machine Translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3486–3497, Online. Association for Computational Linguistics.