20 citations · 58 across the 9 of their papers we have counts for
3 papers · 1 filter
Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages
Maria Glenski, Ellyn Ayton, Robin Cosbey +2
Evaluating model robustness is critical when developing trustworthy models not only to gain deeper understanding of model behavior, strengths, and weaknesses, but also to develop f…
Evaluating Deception Detection Model Robustness To Linguistic Variation
Maria Glenski, Ellyn Ayton, Robin Cosbey +2
With the increasing use of machine-learning driven algorithmic judgements, it is critical to develop models that are robust to evolving or manipulated inputs. We propose an extensi…
Evaluating Neural Machine Comprehension Model Robustness to Noisy Inputs and Adversarial Attacks
Winston Wu, Dustin Arendt, Svitlana Volkova
We evaluate machine comprehension models' robustness to noise and adversarial attacks by performing novel perturbations at the character, word, and sentence level. We experiment wi…