Multi-task Learning for Japanese Predicate Argument Structure Analysis
Table 1: Hyperparameters.
['the dimension of word embeddings  [ITALIC] dw', '300']
The initial values of the other embedding matrices are sampled according to a uniform distribution of [-0.25,0.25]. We convert words appearing more than once in the training set into word vectors and the remaining words into the unknown word vector. We adopt AdaDelta (ϵ=10−6，ρ=0.95) as the optimization method. We set the number of epochs to 20 and evaluate the model with the highest F1 scores on the development set.