nlp:robustness_in_nlp

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Robustness and Brittleness in NLP (and Deep Learning)

Robustness and Brittleness in NLP (and Deep Learning)

Papers

Jia and Liang 2017 - Adversarial Examples for Evaluating Reading Comprehension Systems
Gururangan et al 2018 - Annotation Artifacts in Natural Language Inference Data
Poliak et al 2018 - Hypothesis Only Baselines in Natural Language Inference
Zellers et al 2017 - SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference Introduces Adversarial Filtering to try to eliminate dataset bias
McCoy et al 2019 - Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference
Wee and Ng 2019 - Improving the Robustness of Question Answering Systems to Question Paraphrasing
Si et al 2020 - Benchmarking Robustness of Machine Reading Comprehension Models
Jin et al 2019 - Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment
Ribeiro et al 2020 - Beyond Accuracy: Behavioral Testing of NLP Models with CheckList
Bartolo et al 2020 - Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension Applies adversarial filtering to QA
Srivastava et al 2020 - Robustness to Spurious Correlations via Human Annotations

Conferences, Workshops, and Shared Tasks

People

Related Pages

nlp/robustness_in_nlp.1619681593.txt.gz · Last modified: 2023/06/15 07:36 (external edit)