  Among the most critical limitations of deep learning NLP models are their
lack of interpretability, and their reliance on spurious correlations. Prior
work proposed various approaches to interpreting the black-box models to unveil
the spurious correlations, but the research was primarily used in
human-computer interaction scenarios. It still remains underexplored whether or
how such model interpretations can be used to automatically "unlearn"
confounding features. In this work, we propose influence tuning--a procedure
that leverages model interpretations to update the model parameters towards a
plausible interpretation (rather than an interpretation that relies on spurious
patterns in the data) in addition to learning to predict the task labels. We
show that in a controlled setup, influence tuning can help deconfounding the
model from spurious patterns in data, significantly outperforming baseline
methods that use adversarial training.
