activity
20182021
most citedDouble Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL2021

Ethical-Advice Taker: Do Language Models Understand Natural Language Interventions?

Jieyu Zhao, Daniel Khashabi, Tushar Khot +2

Is it possible to use natural language to intervene in a model's behavior and alter its prediction in a desired way? We investigate the effectiveness of natural language interventi…

cs.CL20211 cited

Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation

Chong Zhang, Jieyu Zhao, Huan Zhang +2

Robustness and counterfactual bias are usually evaluated on a test dataset. However, are these evaluations robust? If the test dataset is perturbed slightly, will the evaluation re…

cs.LG2019

Towards Understanding Gender Bias in Relation Extraction

Andrew Gaut, Tony Sun, Shirlyn Tang +8

Recent developments in Neural Relation Extraction (NRE) have made significant strides towards Automated Knowledge Base Construction (AKBC). While much attention has been dedicated…

cs.CL2019

Examining Gender Bias in Languages with Grammatical Gender

Pei Zhou, Weijia Shi, Jieyu Zhao +4

Recent studies have shown that word embeddings exhibit gender bias inherited from the training corpora. However, most studies to date have focused on quantifying and mitigating suc…

cs.CV2018

Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations

Tianlu Wang, Jieyu Zhao, Mark Yatskar +2

In this work, we present a framework to measure and mitigate intrinsic biases with respect to protected variables --such as gender-- in visual recognition tasks. We show that train…