  As for semantic role labeling (SRL) task, when it comes to utilizing parsing
information, both traditional methods and recent recurrent neural network (RNN)
based methods use the feature engineering way. In this paper, we propose Syntax
Aware Long Short Time Memory(SA-LSTM). The structure of SA-LSTM modifies
according to dependency parsing information in order to model parsing
information directly in an architecture engineering way instead of feature
engineering way. We experimentally demonstrate that SA-LSTM gains more
improvement from the model architecture. Furthermore, SA-LSTM outperforms the
state-of-the-art on CPB 1.0 significantly according to Student t-test
($p<0.05$).
