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Who cites «Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples»

16 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

  1. Unveiling the Achilles'' Heel of NLG Evaluators: A Unified Adversarial Framework Driven by Large Language Models — 2024 · Findings of the Association for Computational Linguistics: ACL 2024
  2. Certified Robustness to Text Adversarial Attacks by Randomized [MASK] — 2023 · Computational Linguistics
  3. Phrase-level Textual Adversarial Attack with Label Preservation — 2022 · Findings of the Association for Computational Linguistics: NAACL 2022
  4. ROSE: Robust Selective Fine-tuning for Pre-trained Language Models — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  5. To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP — 2022 · Computational Linguistics
  6. Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
  7. A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples — our card · 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
  8. Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning — 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
  9. Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble — 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
  10. On the Transferability of Adversarial Attacks against Neural Text Classifier — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  11. Robustness and Adversarial Examples in Natural Language Processing — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts
  12. Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  13. Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples — 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
  14. wan-2022-paeg-phrase-level-adversarial-example
  15. xu-2022-weight-perturbation-as-defense-against
  16. zhang-2022-sharp-search-based-adversarial-attack

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