  Neural networks are prone to learning spurious correlations from biased
datasets, and are thus vulnerable when making inferences in a new target
domain. Prior work reveals spurious patterns via post-hoc model explanations
which compute the importance of input features, and further eliminates the
unintended model behaviors by regularizing importance scores with human
knowledge. However, such regularization technique lacks flexibility and
coverage, since only importance scores towards a pre-defined list of features
are adjusted, while more complex human knowledge such as feature interaction
and pattern generalization can hardly be incorporated. In this work, we propose
to refine a learned model by collecting human-provided compositional
explanations on the models' failure cases. By describing generalizable rules
about spurious patterns in the explanation, more training examples can be
matched and regularized, tackling the challenge of regularization coverage. We
additionally introduce a regularization term for feature interaction to support
more complex human rationale in refining the model. We demonstrate the
effectiveness of the proposed approach on two text classification tasks by
showing improved performance in target domain after refinement.
