Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.18653/v1/D19-1649 | EMNLP/IJCNLP (1) |
DocType | Volume | Citations |
Conference | D19-1 | 2 |
PageRank | References | Authors |
0.36 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianyu Gao | 1 | 2 | 1.38 |
| Xu Han | 2 | 15 | 4.94 |
| Hao Zhu | 3 | 40 | 7.15 |
| Zhiyuan Liu | 4 | 2037 | 123.68 |
| Peng Li | 5 | 146 | 21.34 |
| Maosong Sun | 6 | 2293 | 162.86 |
| Jie Zhou | 7 | 13 | 11.09 |