  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.
