As can be seen, the greater word representation dimensions are generally helpful for both WSJ and CTB on the closed development data (dev), which may match with our intuition that the richer syntactic and semantic knowledge representation for each word is required for parsing.
overfitting was observed when using a 32-dimension hidden vector in both tasks, i.e., drops of performance on the open test data (test) when m = 1024, probably caused by the limited generalization capability in the smaller hidden state size.