Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data
William C. Huang, Haokun Liu, Samuel R. Bowman
Abstract
A growing body of work shows that models exploit annotation artifacts to achieve state-ofthe-art performance on standard crowdsourced benchmarks-datasets collected from crowdworkers to create an evaluation task-while still failing on out-of-domain examples for the same task. Recent work has explored the use of counterfactually-augmented data-data built by minimally editing a set of seed examples to yield counterfactual labels-to augment training data associated with these benchmarks and build more robust classifiers that generalize better. However, We build upon this work by using English natural language inference data to test model generalization and robustness and find that models trained on a counterfactuallyaugmented SNLI dataset do not generalize better than unaugmented datasets of similar size and that counterfactual augmentation can hurt performance, yielding models that are less robust to challenge examples. Counterfactual augmentation of natural language understanding data through standard crowdsourcing techniques does not appear to be an effective way of collecting training data and further innovation is required to make this general line of work viable.