Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification
Table 3: Hyper-parameters of the models built in our experiments.
['[BOLD] Component', '[BOLD] Parameter', '[BOLD] Value']
The 50-dimensional Glove word embeddings released by Pennington et al. For the unknown words, we just replaced them with an unique special token   and fixed its embedding as a zero vector. Therefore, we set N=20 to construct the train-support sets for 5-way and 10-way tasks. In our experiments, grid searches among dc∈{100,150,200,250}, dh∈{100,150,200,250} and R∈{5,10,15} were conducted to determine their optimal values. For optimization, we employed mini-batch stochastic gradient descent (SGD) with the initial learning rate of 0.1. The learning rate was decayed to one tenth every 20,000 steps. And also, dropout layers Hinton et al.