AMR parser with Convolutional Seq2seq

We published the source code for the paper ConvAMR: Abstract Meaning Representation Parsing for Legal Document

@article{viet:2017:SCIDOCA,
  author    = {Lai Dac Viet and
               Vu Trong Sinh and
               Nguyen Le Minh and
               Ken Satoh},
  title     = {ConvAMR: Abstract meaning representation parsing for legal document},
  journal   = {CoRR},
  volume    = {abs/1711.06141},
  year      = {2017},
  url       = {http://arxiv.org/abs/1711.06141},
  archivePrefix = {arXiv},
  eprint    = {1711.06141},
}

Installation

Install torch and fairseq

We recommend to install and run this with Ubuntu 16.04, cuda 8.0, cudnn 5.1.

We recently (June 2019) tested it on Ubuntu 18.04, cuda 8.0, cudnn 5.1. We got a lot of troubles while compiling torch, fairseq and dependencies. If you use Ubuntu 18.04, follow this post to modify torch and fairseq installation scripts.

Torch installation can be found here

Fairseq installation can be found here

Python dependencies

pip install nltk==3.4.3
pip install penman==0.6.2

Train a model

We use following directories in this project:

corpus train/dev/test splits of LDC2014T12 and LDC2017T10

data text-format files, generated from corpus by cmdpreprocess.sh

data-bin binary-format files, generated from corpus by cmdpreprocess.sh

output trained models and logs

tmp parsed amr files, generated by amr/postprocess.py

ROOT
|-corpus
  |-LDC2014T12
    |-training
    |-dev
    |-test
|-data
  |-LDC2014T12.fairseq
|-data-bin
  |-LDC2014T12.fairseq
|-output
  |-LDC2014T12.fairseq.fconv.r0
|-tmp

Step 1: Please copy training/dev/test of LDC2014T12 and LDC2017T10 into corpus directory, accordingly.

Step 2: Preprocess AMR file:

chmod +x cmdpreprocess.sh; ./cmdpreprocess.sh

Step 3: Train model:

chmod +x cmdtrain.sh; ./cmdtrain.sh

Contact

If you find and issues, please raise an issue in github repository or contact us at vietl@uoregon.edu.