  The training of deep-learning-based text classification models relies heavily
on a huge amount of annotation data, which is difficult to obtain. When the
labeled data is scarce, models tend to struggle to achieve satisfactory
performance. However, human beings can distinguish new categories very
efficiently with few examples. This is mainly due to the fact that human beings
can leverage knowledge obtained from relevant tasks. Inspired by human
intelligence, we propose to introduce external knowledge into few-shot learning
to imitate human knowledge. A novel parameter generator network is investigated
to this end, which is able to use the external knowledge to generate relation
network parameters. Metrics can be transferred among tasks when equipped with
these generated parameters, so that similar tasks use similar metrics while
different tasks use different metrics. Through experiments, we demonstrate that
our method outperforms the state-of-the-art few-shot text classification
models.
