1.4k citations · 1.4k across the 3 of their papers we have counts for
3 papers
Gender Bias in Contextualized Word Embeddings
Jieyu Zhao, Tianlu Wang, Mark Yatskar +3
In this paper, we quantify, analyze and mitigate gender bias exhibited in ELMo's contextualized word vectors. First, we conduct several intrinsic analyses and find that (1) trainin…
Efficient Contextual Representation Learning Without Softmax Layer
Liunian Harold Li, Patrick H. Chen, Cho-Jui Hsieh +1
Contextual representation models have achieved great success in improving various downstream tasks. However, these language-model-based encoders are difficult to train due to the l…
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Zou +2
The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text…