  Cross-domain text classification aims to transfer models from label-rich
source domains to label-poor target domains, giving it a wide range of
practical applications. Many approaches promote cross-domain generalization by
capturing domain-invariant features. However, these methods rely on unlabeled
samples provided by the target domains, which renders the model ineffective
when the target domain is agnostic. Furthermore, the models are easily
disturbed by shortcut learning in the source domain, which also hinders the
improvement of domain generalization ability. To solve the aforementioned
issues, this paper proposes TACIT, a target domain agnostic feature
disentanglement framework which adaptively decouples robust and unrobust
features by Variational Auto-Encoders. Additionally, to encourage the
separation of unrobust features from robust features, we design a feature
distillation task that compels unrobust features to approximate the output of
the teacher. The teacher model is trained with a few easy samples that are easy
to carry potential unknown shortcuts. Experimental results verify that our
framework achieves comparable results to state-of-the-art baselines while
utilizing only source domain data.
