This is a fork of OpenNMT-py (https://github.com/OpenNMT/OpenNMT-py), v0.4 with modifications to run experiments on AMR-to-text generation discussed in our paper (LINK).
Follow instructions on README_OpenNMT.md to install
Our models expect linearized and anonymized AMR. The directory NeuralAmrReentrancies contains a modified version of the anonymization system of https://github.com/sinantie/NeuralAmr that preserve reentrancies information required by our graph encoders. Before running the following experiments, use NeuralAmrReentrancies to generate the linearized and anonymized data, follow the instructions at https://github.com/sinantie/NeuralAmr#de-anonymizing-parallel-corpus-eg-ldc-versions.
Follow these instructions to replicate the experiments reported in Table 1 of the paper.
For each experiment, run the preprocessing script, training script and testing script. For sequential and tree encoders, the preprocessing script to use is preproc_amr.sh and the evaluation script is predict.sh. For graph encoders, use preproc_amr_reent.sh and predict_reent.sh. In the following we report the training scripts to use for each experiment. Refer to the paper for the explanation of each model.
Seq: train_amr_seq.sh
SeqTreeLSTM: train_amr_tree_seq_treelstm.sh
TreeLSTMSeq: train_amr_tree_treelstm_seq.sh
TreeLSTM: train_amr_tree_treelstm.sh
SeqGCN: train_amr_tree_seq_gcn.sh
GCNSeq: train_amr_tree_gcn_seq.sh
GCN: train_amr_tree_gcn.sh
SeqGCN: train_amr_graph_seq_gcn.sh
GCNSeq: train_amr_graph_gcn_seq.sh
GCN: train_amr_graph_gcn.sh
Use recomputeMetrics.sh in https://github.com/sinantie/NeuralAmr to evaluate the models.
See contrastive_examples/
@inproceedings{damonte2019gen,
title={Structural Neural Encoders for AMR-to-text Generation},
author={Damonte, Marco and Cohen, Shay B},
booktitle={Proceedings of NAACL},
year={2019}
}