We present FewRel 2.0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances? (2) Can they detect none-of-the-above (NOTA) relations? To construct FewRel 2.0, we build upon the FewRel dataset (Han et al., 2018) by adding a new test set in a quite different domain, and a NOTA relation choice. With the new dataset and extensive experimental analysis, we found (1) that the state-of-the-art few-shot relation classification models struggle on these two aspects, and (2) that the commonly-used techniques for domain adaptation and NOTA detection still cannot handle the two challenges well. Our research calls for more attention and further efforts to these two real-world issues. All details and resources about the dataset and baselines are released at https: //github.com/thunlp/fewrel.
@article{arxiv.1910.07124,
title = {FewRel 2.0: Towards More Challenging Few-Shot Relation Classification},
author = {Tianyu Gao and Xu Han and Hao Zhu and Zhiyuan Liu and Peng Li and Maosong Sun and Jie Zhou},
journal= {arXiv preprint arXiv:1910.07124},
year = {2019}
}