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

ConvAMR: Abstract meaning representation parsing for legal document