Abstract
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. This survey will 1) compare the three lines of research under a unifying definition; 2) summarize their data-generating processes and evaluation protocols for each line of research; and 3) emphasize the challenges and opportunities for future work.
Keywords
- Survey: Natural Language Processing
- Survey: Machine Learning
- Survey: Uncertainty in AI
- Survey: AI Ethics, Trust, Fairness
