Two Local Models for Neural Constituent Parsing
Table 1: Hyper-parameters for training.
['[BOLD] hyper-parameter Word embeddings', '[BOLD] value English: 100 Chinese: 80', '[BOLD] hyper-parameter Word LSTM layers', '[BOLD] value 2']
Hyper-parameters. These values are tuned using the corresponding development sets. We optimize our models with stochastic gradient descent (SGD). The initial learning rate is 0.1. Our model are initialized with pretrained word embeddings both for English and Chinese. The pretrained word embeddings are the same as those used in \newcitedyer2016rnng. For Chinese, we find that 0.3 is a good choice for the dropout probability. The number of training epochs is decided by the evaluation performances on development set. In particular, we perform evaluations on development set for every 10,000 examples. The training procedure stops when the results of next 20 evaluations do not become better than the previous best record.