  Dataset bias, i.e., the over-reliance on dataset-specific literal heuristics,
is getting increasing attention for its detrimental effect on the
generalization ability of NLU models. Existing works focus on eliminating
dataset bias by down-weighting problematic data in the training process, which
induce the omission of valid feature information while mitigating bias. In this
work, We analyze the causes of dataset bias from the perspective of causal
inference and propose CausalAPM, a generalizable literal disentangling
framework to ameliorate the bias problem from feature granularity. The proposed
approach projects literal and semantic information into independent feature
subspaces, and constrains the involvement of literal information in subsequent
predictions. Extensive experiments on three NLP benchmarks (MNLI, FEVER, and
QQP) demonstrate that our proposed framework significantly improves the OOD
generalization performance while maintaining ID performance.
