Few-shot relation classification describes a circumstance where a model is required to classify new-coming query instances after meeting only few support instances during testing. In this paper, we place a challenging restriction to conventional few-shot relation classification by additionally limiting the amount of training data . We also propose a few-shot learning framework for relation classification, which is particularly powerful when the training data is very small. In our framework, models not only strive to classify query instances, but also seek underlying knowledge within support instances to obtain better instance representations. The framework also includes a method for aggregating cross-domain knowledge into models by open-source task enrichment. Additionally, we construct a brand new dataset: the TinyRel-CM dataset, a few-shot relation classification dataset in health domain with limited training data and challenging relation classes. Experimental results demonstrate that our framework brings performance gains for most underlying classification models, and outperforms the state-of-the-art results given little training data(e.g., on TinyRel-CM dataset and FewRel-dataset Han et al., 2018%DIFDELCMD < ] %%% with reduced training set), and achieves competitive results with sufficiently large training data (e.g., on FewRel dataset with full training data) .
Few-shot relation classification seeks to classify incoming query instances after meeting only few support instances . This ability is gained by training with large amount of in-domain annotated data. In this paper, we tackle an even harder problem by further limiting the amount of data available at training time. We propose a few-shot learning framework for relation classification, which is particularly powerful when the training data is very small. In this framework, models not only strive to classify query instances, but also seek underlying knowledge about the support instances to obtain better instance representations. The framework also includes a method for aggregating cross-domain knowledge into models by open-source task enrichment. Additionally, we construct a brand new dataset: the TinyRel-CM dataset, a few-shot relation classification dataset in health domain with purposely small training data and challenging relation classes. Experimental results demonstrate that our framework brings performance gains for most underlying classification models, outperforms the state-of-the-art results given %DIFDELCMD < ] %%% small training data, and achieves competitive results with sufficiently large training data .
R
describes a circumstance where a model is required to classify new-coming
seeks to classify incoming
R
during testing.
. This ability is gained by training with large amount of in-domain annotated data.
R
place a challenging restriction to conventional few-shot relation classification by additionally
tackle an even harder problem by further
R
training data . We also
data available at training time. We
R
our
this
R
within
about the
R
limited
purposely small
D
and
D
little training data(e.g., on TinyRel-CM dataset and FewRel-dataset
D
Han et al., 2018
R
with reduced training set), and
small training data, and
D
(e.g., on FewRel dataset with full training data)