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Found 4 papers, 3 papers with code
Addressing Both Statistical and Causal Gender Fairness in NLP Models
Statistical fairness stipulates equivalent outcomes for every protected group, whereas causal fairness prescribes that a model makes the same prediction for an individual regardless of their protected characteristics.
Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models
Traditional (fickle) adversarial examples involve finding a small perturbation that does not change an input's true label but confuses the classifier into outputting a different prediction.
Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory
Most NLP datasets are manually labeled, so suffer from inconsistent labeling or limited size.
Pointwise Paraphrase Appraisal is Potentially Problematic
The prevailing approach for training and evaluating paraphrase identification models is constructed as a binary classification problem: the model is given a pair of sentences, and is judged by how accurately it classifies pairs as either paraphrases or non-paraphrases.
