Before revision: In attempts to produce machine learning models less reliant on spurious patterns in training data , researchers have recently proposed  a human-in-the-loop process for generating counterfactually augmented datasets. As applied in NLP, given some documents and their (initial) labels, humans are tasked with revising the text to make a (given) counterfactual label applicable.
After revision: In attempts to produce machine learning models less reliant on spurious patterns in training data , researchers have recently proposed a human-in-the-loop process in which given some documents and their (initial) labels, humans are tasked with revising the text to make a (given) counterfactual label applicable.