| Challenge: | Neural machine translation (NMT) models suffer from noisy perturbations in the input . a gradient-based method to craft adversarial examples informed by the translation loss is proposed . |
| Approach: | They propose an approach to improve the robustness of NMT models by attacking the translation model with adversarial source examples and defending the model with a target input. |
| Outcome: | The proposed approach improves translation performance and robustness on clean inputs and higher on noisy data. |
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Towards Robust Neural Machine Translation (P18-1)
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| Challenge: | Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation models. |
| Approach: | They propose adversarial stability training to make encoder and decoder robust to perturbations by enabling them to behave similarly for the original input and its perturbed counterpart. |
| Outcome: | The proposed approach improves translation quality and robustness over strong models on Chinese-English, English-German and English-French translation tasks. |
A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translation (2024.eacl-long)
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| Challenge: | Extensive research has been devoted to adversarial attacks against NMT models . perturbations of inputs can mislead the target model, resulting in incorrect outputs . |
| Approach: | They propose an adversarial attack framework that alters the class of output translations of an NMT model and a classifier to craft adversarials whose translations belong to a different class . |
| Outcome: | The proposed approach has a more substantial effect on the translation by altering the overall meaning, which leads to a different class determined by an oracle classifier. |
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-Translation (D19-55)
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| Challenge: | Neural Machine Translation models are sensitive to noise in the input data. |
| Approach: | They propose new methods to extend limited noisy data and further improve NMT robustness to noise while keeping the models small. |
| Outcome: | The proposed methods extend limited noisy data and improve robustness to noise while keeping the models small. |
Crafting Adversarial Examples for Neural Machine Translation (2021.acl-long)
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| Challenge: | Effective adversary generation for neural machine translation is crucial for robust systems. |
| Approach: | They propose to leverage round-trip translation technique to build valid metrics for evaluating NMT adversarial attacks. |
| Outcome: | The proposed method could break the state-of-art NMT models with small perturbations. |
Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training (2022.coling-1)
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Zhongjian Miao, Xiang Li, Liyan Kang, Wen Zhang, Chulun Zhou, Yidong Chen, Bin Wang, Min Zhang, Jinsong Su
| Challenge: | Existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples. |
| Approach: | They propose an iterative scheduled data-switch training framework to mitigate this problem by injecting noise into authentic examples and indiscriminately exploiting two types of examples. |
| Outcome: | The proposed model outperforms several competitive benchmarks on four translation benchmarks. |
Effective Adversarial Regularization for Neural Machine Translation (P19-1)
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| Challenge: | Existing (small) perturbations that induce a critical prediction error in machine learning models are often referred to as adversarial examples. |
| Approach: | They propose to use adversarial perturbations to regularize text classification tasks by adding adversarials to a typical NMT model structure. |
| Outcome: | The proposed method significantly improves performance of NMT models, such as LSTM-based and Transformer-based models. |
Neural Machine Translation of Text from Non-Native Speakers (N19-1)
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| Challenge: | Neural Machine Translation (NMT) systems are known to degrade when confronted with noisy data. |
| Approach: | They propose to augment training data with sentences containing artificially-introduced grammatical errors to make the system more robust to such errors. |
| Outcome: | The proposed approach recovers 1.0 BLEU out of 2.4 BLUE lost due to grammatical errors on a set of Spanish translations of the JFLEG grammar error correction corpus. |
Addressing the Vulnerability of NMT in Input Perturbations (2021.naacl-industry)
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| Challenge: | Recent advances in NMT have improved translation quality but are vulnerable to input perturbations. |
| Approach: | They propose a method to reduce the effect of noisy inputs by using a Context-Enhanced Reconstruction approach. |
| Outcome: | The proposed approach improves robustness on Chinese-English and French-English translation tasks. |
On Adversarial Examples for Character-Level Neural Machine Translation (C18-1)
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| Challenge: | Using adversarial examples to measure robustness of deep learning models has become a standard procedure due to the difficulty of creating white-box adversarials for discrete text input. |
| Approach: | They propose two novel attacks which aim to remove or change a word in a translation, rather than simply break the NMT. |
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A Simple and Effective Approach to Coverage-Aware Neural Machine Translation (P18-2)
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| Challenge: | Neural Machine Translation (NMT) models are used to solve translation problems using long-term models. |
| Approach: | They propose a method to seek a better balance between model confidence and length preference for Neural Machine Translation. |
| Outcome: | The proposed model improves on Chinese-English and English-German translation tasks. |