  Adversarial robustness, domain generalization and dataset biases are three
active lines of research contributing to out-of-distribution (OOD) evaluation
on neural NLP models. However, a comprehensive, integrated discussion of the
three research lines is still lacking in the literature. In this survey, we 1)
compare the three lines of research under a unifying definition; 2) summarize
the data-generating processes and evaluation protocols for each line of
research; and 3) emphasize the challenges and opportunities for future work.
