Explaining the Efficacy of Counterfactually Augmented Data

ICLR 2021, 2021.

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We present a framework for thinking about counterfactually augmented data and make strides towards understanding its benefits in out-of-domain generalization.

Abstract:

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

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