| Challenge: | evaluating model robustness to adversarial attacks can provide deeper understanding of how deep neural networks work and what kind of linguistic information is actually captured by neural networks. |
| Approach: | They propose a method for strategic sentence-level perturbations to evaluate model robustness to adversarial attacks using character and word perturbations. |
| Outcome: | The proposed model improves model performance during adversarial attacks by using ensembles and predicts errors in adversarials. |
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Evaluating the Robustness of Neural Language Models to Input Perturbations (2021.emnlp-main)
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| Challenge: | High-performance neural language models have achieved state-of-the-art results on a wide range of NLP tasks, but results for common benchmark datasets often do not reflect model reliability and robustness when applied to noisy, real-world data. |
| Approach: | They propose to implement character-level and word-level perturbation methods to simulate scenarios in which input texts may be slightly noisy or different from the data distribution on which NLP systems were trained. |
| Outcome: | The proposed methods simulate scenarios in which input texts may be slightly noisy or different from the data distribution on which NLP systems were trained. |
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples (2020.acl-main)
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| Challenge: | Previously studies focused on semantic tasks such as sentiment analysis, question answering and reading comprehension. |
| Approach: | They propose two approaches to study where and how adversarial examples exist in dependency parsing . they use a state-of-the-art parser to find adversarials in existing texts . |
| Outcome: | The proposed approaches show that adversarial examples exist in dependency parsing . they show that up to 77% of input examples admit adversarials . |
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. |
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models (2023.eacl-main)
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| Challenge: | Pretrained language models have achieved super-human performances on many Machine Reading Comprehension (MRC) benchmarks. |
| Approach: | They propose to fine-tune three state-of-the-art language models on SQuAD 1.1 or SQu AD 2.0 and then evaluate their robustness under adversarial attacks. |
| Outcome: | The proposed model is able to perform better under adversarial attacks than model fine-tuned on SQuAD 1.1 or 2.0. |
Interpreting the Robustness of Neural NLP Models to Textual Perturbations (2022.findings-acl)
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| Challenge: | Modern Natural Language Processing models are sensitive to input perturbations and their performance can decrease when applied to noisy data. |
| Approach: | They propose to explain the extent to which a model is affected by an unseen textual perturbation by the learnability of the perturbation. |
| Outcome: | The proposed model is better at identifying a perturbation (higher learnability) but worse at ignoring it (lower robustness). |
Robust Machine Comprehension Models via Adversarial Training (N18-2)
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| Challenge: | Existing models for the Stanford Question Answering Dataset suffer from a 50% decrease in F1 score during adversarial evaluation based on AddSent. |
| Approach: | They propose an alternative adversary-generation algorithm, AddSentDiverse, that significantly increases the variance within the adversarial training data by providing effective examples that punish the model for making certain superficial assumptions. |
| Outcome: | The proposed algorithm can achieve a 36.5% increase in F1 score while maintaining performance on the regular SQuAD task. |
Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation (2021.naacl-main)
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| Challenge: | Recent studies show that NLP models are vulnerable to adversarial perturbations such as synonym substitutions or syntax-guided paraphrasing. |
| Approach: | They propose a “double perturbation” framework to uncover model weaknesses beyond the test dataset. |
| Outcome: | The proposed attack achieves high success rates on both original and robustly trained CNNs and Transformers. |
A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples (2021.findings-acl)
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| Challenge: | Neural network-based models have been successful in a wide range of NLP tasks, but their performance is undermined by adversarial examples that would pose no confusion for humans. |
| Approach: | They propose a method to generate high-quality adversarial examples with a higher number of candidate generators and stricter filters and then verify their quality using automatic and human evaluations. |
| Outcome: | The proposed method improves the robustness of English parsing models by relying on adversarial training and model ensembling. |
Robustness and Adversarial Examples in Natural Language Processing (2021.emnlp-tutorials)
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| Challenge: | This tutorial aims to raise awareness of practical concerns about NLP robustness . it aims at addressing the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
| Approach: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it reviews recent studies on analyzing the weakness of NLP systems when facing adversarial inputs . |
| Outcome: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it will examine the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
Evaluating Robustness to Input Perturbations for Neural Machine Translation (2020.acl-main)
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| Challenge: | Recent work has shown that Neural Machine Translation models are brittle to small perturbations in the input. |
| Approach: | They propose to use subword regularization to measure the relative degradation and changes in translation when perturbations are added to the input. |
| Outcome: | The proposed measures show that the models are more robust to perturbations when subword regularization methods are used. |