ConvAMR: Abstract meaning representation parsing for legal document
This work addresses parsing challenges in legal documents, though it appears incremental as it builds on existing seq2seq and linearization approaches.
The researchers tackled abstract meaning representation parsing for legal documents by developing a convolutional seq2seq model with an improved graph linearization technique, achieving significantly better performance and faster speed than previous methods on the LDC2014T12 dataset.
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.