A Simple Baseline for Cross-Domain Few-Shot Text Classification

Chen Zhang, Dawei Song

Abstract: Few-shot text classification has been largely explored due to its remarkable few-shot generalization ability to in-domain novel classes. Yet, the generalization ability of existing models to cross-domain novel classes has seldom be studied. To fill the gap, we investigate a new task, called cross-domain few-shot text classification (XFew) and present a simple baseline that witnesses an appealing cross-domain generalization capability while retains a nice in-domain generalization capability. Experiments are conducted on two datasets under both in-domain and cross-domain settings. The results show that current few-shot text classification models lack a mechanism to account for potential domain shift in the XFew task. In contrast, our proposed simple baseline achieves surprisingly superior results in comparison with other models in cross-domain scenarios, confirming the need of further research in the XFew task and providing insights for possible directions. (The code and datasets are available at https://github.com/GeneZC/XFew ).