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• W3034397670 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." @default.
• W3034397670 created "2020-06-19" @default.
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• W3034397670 date "2020-01-01" @default.
• W3034397670 modified "2023-09-23" @default.
• W3034397670 title "A Reinforced Generation of Adversarial Examples for Neural Machine Translation" @default.
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• W3034397670 doi "https://doi.org/10.18653/v1/2020.acl-main.319" @default.
• W3034397670 hasPublicationYear "2020" @default.
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