  Pre-trained language models have been successful on text classification
tasks, but are prone to learning spurious correlations from biased datasets,
and are thus vulnerable when making inferences in a new domain. Prior work
reveals such spurious patterns via post-hoc explanation algorithms which
compute the importance of input features. Further, the model is regularized to
align the importance scores with human knowledge, so that the unintended model
behaviors are eliminated. However, such a 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 language model for a target domain by
collecting human-provided compositional explanations regarding observed biases.
By parsing these explanations into executable logic rules, the human-specified
refinement advice from a small set of explanations can be generalized to more
training examples. We additionally introduce a regularization term allowing
adjustments for both importance and interaction of features to better rectify
model behavior. We demonstrate the effectiveness of the proposed approach on
two text classification tasks by showing improved performance in target domain
as well as improved model fairness after refinement.
