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Robustness Tests of NLP Machine Learning Models: Search and Semantically Replace

arXiv 2104.09978 ↗First posted 20 Apr 2021cs.CL cs.AI cs.LGcited by 0

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Abstract (arXiv)

This paper proposes a strategy to assess the robustness of different machine learning models that involve natural language processing (NLP). The overall approach relies upon a Search and Semantically Replace strategy that consists of two steps: (1) Search, which identifies important parts in the text; (2) Semantically Replace, which finds replacements for the important parts, and constrains the replaced tokens with semantically similar words. We introduce different types of Search and Semantically Replace methods designed specifically for particular types of machine learning models. We also investigate the effectiveness of this strategy and provide a general framework to assess a variety of machine learning models. Finally, an empirical comparison is provided of robustness performance among three different model types, each with a different text representation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 23 references that are in the map. First posted on arXiv 20 Apr 2021.

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References · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  4. 2013Intriguing properties of neural networkscited by 5,114
  5. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  6. 2019Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailmentcited by 663
  7. 2017Adversarial Examples for Evaluating Reading Comprehension Systemscited by 568
  8. 2020BERT-ATTACK: Adversarial Attack Against BERT Using BERTcited by 488
  9. 2016Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samplescited by 439
  10. 2018Generating Natural Language Adversarial Examplescited by 422
  11. 2020BAE: BERT-based Adversarial Examples for Text Classificationcited by 316
  12. 2017Deep Text Classification Can be Fooledcited by 105
4 more
  1. 2017Towards Crafting Text Adversarial Samplescited by 55
  2. 2016"What is Relevant in a Text Document?": An Interpretable Machine Learning Approachcited by 53
  3. 2020TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLPcited by 45
  4. 2018Understanding Convolutional Neural Networks for Text Classificationcited by 36

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