Out-of-Distribution Generalization in Text Classification: Past,
  Present, and Future
Linyi Yang Yaoxiao Song Xuan Ren Chenyang Lyu Yidong Wang Lingqiao Liu Jindong Wang Jennifer Foster Yue Zhang
Machine learning (ML) systems in natural language processing (NLP) face
significant challenges in generalizing to out-of-distribution (OOD) data, where
the test distribution differs from the training data distribution. This poses
important questions about the robustness of NLP models and their high accuracy,
which may be artificially inflated due to their underlying sensitivity to
systematic biases. Despite these challenges, there is a lack of comprehensive
surveys on the generalization challenge from an OOD perspective in text
classification. Therefore, this paper aims to fill this gap by presenting the
first comprehensive review of recent progress, methods, and evaluations on this
topic. We furth discuss the challenges involved and potential future research
directions. By providing quick access to existing work, we hope this survey
will encourage future research in this area.