17 citations · 24 across the 7 of their papers we have counts for
4 papers · 1 filter
UnQovering Stereotyping Biases via Underspecified Questions
Tao Li, Tushar Khot, Daniel Khashabi +2
While language embeddings have been shown to have stereotyping biases, how these biases affect downstream question answering (QA) models remains unexplored. We present UNQOVER, a g…
On Data Augmentation for Extreme Multi-label Classification
Danqing Zhang, Tao Li, Haiyang Zhang +1
In this paper, we focus on data augmentation for the extreme multi-label classification (XMC) problem. One of the most challenging issues of XMC is the long tail label distribution…
OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings
Sunipa Dev, Tao Li, Jeff M Phillips +1
Language representations are known to carry stereotypical biases and, as a result, lead to biased predictions in downstream tasks. While existing methods are effective at mitigatin…
Structured Tuning for Semantic Role Labeling
Tao Li, Parth Anand Jawale, Martha Palmer +1
Recent neural network-driven semantic role labeling (SRL) systems have shown impressive improvements in F1 scores. These improvements are due to expressive input representations, w…