We propose a methodology to produce contrastive explanations for classification models by modifying theвЂ¦ more They are more inherently intuitive to humans to both produce and comprehend. 2021 Contrastive explanations clarify why an event occurred in contrast to another.

Contrastive Explanations for Model Interpretability Alon Jacovi, Swabha Swayamdipta, Shauli Ravfogel, Yanai Elazar, Yejin Choi, Yoav Goldberg EMNLP.Recently, pre-trained language model-based approaches have boosted performance on some WS benchmarks but the source of improvement is still not clear. 2021 The Winograd Schema (WS) has been proposed as a test for measuring commonsense capabilities of models.Back to Square One: Bias Detection, Training and Commonsense Disentanglement in the Winograd Schema Yanai Elazar, Hongming Zhang, Yoav Goldberg, Dan Roth EMNLP.While such attacks have been extensively explored for the purpose of evaluating model robustness, their utility forвЂ¦ more 2021 Discrete adversarial attacks are symbolic perturbations to a language input that preserve the output label but lead to a prediction error.Achieving Model Robustness through Discrete Adversarial Training Maor Ivgi, Jonathan Berant EMNLP.2021 Constructing benchmarks that test the abilities of modern natural language un1 derstanding models is difficult вЂ“ pre-trained language models exploit artifacts in 2 benchmarks to achieve human parity, but still fail on adversarial examples and make 3 errorsвЂ¦ more.CommonsenseQA 2.0: Exposing the Limits of AI through Gamification Alon Talmor, Ori Yoran, Ronan Le Bras, Chandrasekhar Bhagavatula, Yoav Goldberg, Yejin Choi, Jonathan Berant NeurIPS.

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