Practical Benefits of Feature Feedback Under Distribution Shift

In attempts to develop sample-efficient algorithms, researcher have explored myriad mechanisms for collecting and exploiting feature feedback, auxiliary annotations provided for training (but not test) instances that highlight salient evidence. Examples include bounding boxes around objects and salient spans in text. Despite its intuitive appeal, feature feedback has not delivered significant gains in practical problems as assessed on iid holdout sets. However, recent works on counterfactually augmented data suggest an alternative benefit of supplemental annotations: lessening sensitivity to spurious patterns and consequently delivering gains in out-of-domain evaluations. Inspired by these findings, we hypothesize that while the numerous existing methods for incorporating feature feedback have delivered negligible in-sample gains, they may nevertheless generalize better out-of-domain. In experiments addressing sentiment analysis, we show that feature feedback methods perform significantly better on various natural out-of-domain datasets even absent differences on in-domain evaluation. By contrast, on natural language inference tasks, performance remains comparable. Finally, we compare those tasks where feature feedback does (and does not) help.