MICK: A Meta-Learning Framework for Few-shot Relation Classification with Little Training Data
Table 5: Training task settings over shrunken training set.
['[BOLD] % of full training set', '[BOLD] #cls.', '[BOLD] #inst./cls.', '[BOLD] Training task']
We first conduct experiments with little training data. On the TinyRel-CM Dataset, for each group of relation classes, we adopt n-way 5-shot, n-way 10-shot and n-way 15-shot test configurations, where n is the number of classes within the group. During training episodes, we conduct 5-way 15-shot training tasks. Thus totally 6 experiments are done on TinyRel-CM dataset. On the FewRel dataset, we modify the training set by shrinking the number of relation classes and instances per class to various extent. This aims to show not only the effect of our framework under little training data but also the peformance trends of models with the change of data size.