| 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. |
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Adversarial Subword Regularization for Robust Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Existing methods for segmenting words into subword units are not robust enough to handle multiple subword candidates. |
| Approach: | They propose to regularize subword segmentations that maximize the translation loss by using gradient signals during training to prevent erroneous segmentations of unseen words. |
| Outcome: | The proposed method improves the performance of NMT models on low-resource and out-domain datasets. |
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. |
Effectively pretraining a speech translation decoder with Machine Translation data (2020.emnlp-main)
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| Challenge: | Existing approaches to improve the performance of AST systems are based on pretraining the encoder parameters using an ASR model, but using a pretrained MT decoder is not beneficial or improves the results. |
| Approach: | They propose to use an adversarial regularizer to bring the encoder representations of the ASR and NMT tasks closer even though they are in different modalities. |
| Outcome: | The proposed model can be pre-trained using the Automatic Speech Recognition (ASR) task even in different languages and improves in low resource settings. |
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. |
Robust Neural Machine Translation with Doubly Adversarial Inputs (P19-1)
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| 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. |
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. |
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. |
| Outcome: | The proposed attacks are significantly stronger than their black-box counterparts in different attack scenarios, showing more serious vulnerabilities than previously known. |
AdvAug: Robust Adversarial Augmentation for Neural Machine Translation (2020.acl-main)
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| Challenge: | Recent work in neural machine translation has led to dramatic improvements in both research and commercial systems. |
| Approach: | They propose a adversarial augmentation method for Neural Machine Translation that minimizes vicinal risk over virtual sentences . they use a novel vicinity distribution for adversarials to describe a smooth interpolated embedding space . |
| Outcome: | The proposed method outperforms the current method on Chinese-English, English-French, and English-German translation benchmarks. |
Impact of Adversarial Training on Robustness and Generalizability of Language Models (2023.findings-acl)
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| Challenge: | Adversarial training is widely acknowledged as the most effective defense against adversarial attacks, but achieving both robustness and generalization requires a trade-off. |
| Approach: | They propose to compare pre-training data augmentation and training time input perturbations with embedding space perturbations to find out whether they improve generalization. |
| Outcome: | The proposed methods improve generalization and robustness of the trained models. |
A Survey of Domain Adaptation for Neural Machine Translation (C18-1)
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| Challenge: | Neural machine translation (NMT) is a deep learning based approach for machine translation. |
| Approach: | They propose to use a deep learning approach to train machine translation in scenarios where large-scale parallel corpora are available. |
| Outcome: | The proposed approach yields the state-of-the-art translation performance in resource rich scenarios. |