Lightweight, Dynamic Graph Convolutional Networksfor AMR-to-Text Generation (EMNLP2020)

Dependencies

The model requires:

• Python3
• MXNet 1.5.0
• Sockeye 1.18.56 (NMT framework based on MXNet)
• CUDA

Installation

GPU

If you want to run sockeye on a GPU you need to make sure your version of Apache MXNet Incubating contains the GPU bindings. Depending on your version of CUDA you can do this by running the following:

> pip install -r requirements/requirements.gpu-cu${CUDA_VERSION}.txt
> pip install .

Training

To train the LDGCN model, run (e.g., for AMR2015):

./train_amr15gc.sh

Decoding

When we finish the training, we can use the trained model to decode on the test set, run:

./decode_amr15.sh

This will use the last checkpoint by default. Use --checkpoints to specify a model checkpoint file.

Postprocessing

We use BPE code. In the postprocessing stage, we need to merge them into natural language sequence for evaluation, run:

./merge_amr15.sh

Evaluation

For BLEU score evaluation, run:

./eval_amr15_bleu.sh