Knowledge Guided Metric Learning for Few-Shot Text Classification
Table 1: Comparison of mean accuracy (%) on ARSC dataset
['[BOLD] Model', '[BOLD] Mean Acc']
We observe that our method achieves the best results amongst all meta-learning models. Compared with P-MAML, our model not only achieve better performance, but also does exempt from requiring backpropagation to update parameters during testing. Both Induction Network and Relation Network use a single metric to measure the similarity. Compared with these methods, we attribute the improvements of our model to the fact that our model can adapt to diverse tasks with diverse metrics. Compared with ROBUSTTC-FSL, our model leverages knowledge to get implicit task clusters and is trained in an end-to-end manner, which can mitigate error propagation.