---
title: "Who cites Evaluating and Enhancing the Robustness of…"
description: "The papers of the Elamur Research library that cite «Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with…"
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---
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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](https://md.research.elamur.ai/free/en/papers/zheng-2020-evaluating-enhancing-robustness-neural-network/)

1. [Unveiling the Achilles” Heel of NLG Evaluators: A Unified Adversarial Framework Driven by Large Language Models](https://aclanthology.org/2024.findings-acl.80/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
2. [Certified Robustness to Text Adversarial Attacks by Randomized MASK](https://aclanthology.org/2023.cl-2.5/) *— 2023 · Computational Linguistics*
3. [PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation](https://aclanthology.org/2022.coling-1.451/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
4. [Phrase-level Textual Adversarial Attack with Label Preservation](https://aclanthology.org/2022.findings-naacl.83/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
5. [ROSE: Robust Selective Fine-tuning for Pre-trained Language Models](https://aclanthology.org/2022.emnlp-main.186/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
6. [SHARP: Search-Based Adversarial Attack for Structured Prediction](https://aclanthology.org/2022.findings-naacl.71/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
7. [To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP](https://aclanthology.org/2022.cl-1.2/) *— 2022 · Computational Linguistics*
8. [Towards Adversarially Robust Text Classifiers by Learning to Reweight Clean Examples](https://aclanthology.org/2022.findings-acl.134/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
9. [Weight Perturbation as Defense against Adversarial Word Substitutions](https://aclanthology.org/2022.findings-emnlp.523/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
10. [A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples](https://md.research.elamur.ai/free/en/papers/findings-2021-wang-closer-look-into-robustness-neural/) *— our card · 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
11. [Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning](https://aclanthology.org/2021.findings-acl.137/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
12. [Defense against Synonym Substitution-based Adversarial Attacks via Dirichlet Neighborhood Ensemble](https://aclanthology.org/2021.acl-long.426/) *— 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)*
13. [On the Transferability of Adversarial Attacks against Neural Text Classifier](https://aclanthology.org/2021.emnlp-main.121/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
14. [Robustness and Adversarial Examples in Natural Language Processing](https://aclanthology.org/2021.emnlp-tutorials.5/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts*
15. [Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution](https://aclanthology.org/2021.emnlp-main.251/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
16. [Improving Grammatical Error Correction Models with Purpose-Built Adversarial Examples](https://aclanthology.org/2020.emnlp-main.228/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*