  Convolutional neural networks (CNN) have recently achieved remarkable
performance in a wide range of applications. In this research, we equip
convolutional sequence-to-sequence (seq2seq) model with an efficient graph
linearization technique for abstract meaning representation parsing. Our
linearization method is better than the prior method at signaling the turn of
graph traveling. Additionally, convolutional seq2seq model is more appropriate
and considerably faster than the recurrent neural network models in this task.
Our method outperforms previous methods by a large margin on both the standard
dataset LDC2014T12. Our result indicates that future works still have a room
for improving parsing model using graph linearization approach.
