A Survey on Out-of-Distribution Evaluation of Neural NLP Models

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. 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.

References in corpus (2)

• Explaining and Harnessing Adversarial Examples
• Adversarial Texts with Gradient Methods

Cited by in corpus (1)

• BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendations