Search Results for author: Ben Packer

Found 4 papers, 1 papers with code

Can We Improve Model Robustness through Secondary Attribute Counterfactuals?

no code implementations EMNLP 2021 Ananth Balashankar, Xuezhi Wang, Ben Packer, Nithum Thain, Ed Chi, Alex Beutel

By implementing RDI in the context of toxicity detection, we find that accounting for secondary attributes can significantly improve robustness, with improvements in sliced accuracy on the original dataset up to 7% compared to existing robustness methods.

Coreference Resolution Data Augmentation +1

Flexible text generation for counterfactual fairness probing

no code implementations NAACL (WOAH) 2022 Zee Fryer, Vera Axelrod, Ben Packer, Alex Beutel, Jilin Chen, Kellie Webster

A common approach for testing fairness issues in text-based classifiers is through the use of counterfactuals: does the classifier output change if a sensitive attribute in the input is changed?

Fairness Text Generation

Causally motivated Shortcut Removal Using Auxiliary Labels

1 code implementation13 May 2021 Maggie Makar, Ben Packer, Dan Moldovan, Davis Blalock, Yoni Halpern, Alexander D'Amour

Shortcut learning, in which models make use of easy-to-represent but unstable associations, is a major failure mode for robust machine learning.

Causal Inference Disentanglement +1

CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation

no code implementations EMNLP 2020 Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed Chi

Experiments on real-world NLP datasets demonstrate that our method can generate more diverse and fluent adversarial texts, compared to many existing adversarial text generation approaches.

Adversarial Text Sentiment Analysis +1

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