Few-shot relation classification (RC) is one of the critical problems in machine learning. Current research merely focuses on the set-ups that both training and testing are from the same domain. However, in practice, this assumption is not always guaranteed. In this study, we present a novel model that takes into consideration the afore-mentioned cross-domain situation. Not like previous models, we only use the source domain data to train the prototypical networks and test the model on target domain data. A meta-based adversarial training framework (MBATF) is proposed to fine-tune the trained networks for adapting to data from the target domain. Empirical studies confirm the effectiveness of the proposed model.
@article{arxiv.2202.05713,
title = {Cross Domain Few-Shot Learning via Meta Adversarial Training},
author = {Jirui Qi and Richong Zhang and Chune Li and Yongyi Mao},
journal= {arXiv preprint arXiv:2202.05713},
year = {2022}
}