This is the official implementation of AMR Parsing with Causal Hierarchical Attention and Pointers, presented at EMNLP 2023.

chmod +x install.sh
. ./install.sh

The installation script will create a new conda environment, named as chap_amr, and compile some c++ extension, developed based on Transformer Grammars.

First, prepare AMR graph banks using the script in amrlib: https://github.com/bjascob/amrlib/blob/master/scripts/33_Model_Parse_XFM/10_Collect_AMR_Data.py. You will obtain a directory tdata_xfm in amrlib's working directory. Move it to <project root>/data/AMR3.0/tdata_xfm (change to AMR2.0 if processing the AMR 2.0 graph bank; and you don't need this step when processing OOD data).

Then, run scripts/10_prepare_data.sh at the project root. It contains commands of processing all datasets. So you may just need some of them according to your usage.

python src/train.py experiment=<model config>

# our best model
python src/train.py experiment=t2g_point2tgt_paware_strict_adapter

We use hydra to configure training and models. This project is based on the lightning-hydra-template. experiment will use the config files in configs/experiment, in which import the corresponding config files in configs/model. The config file name is organized as follows:

• strict means that the pointers of coref layer are constrained to valid values.

In the training script, a prediction on the test set will be stored under the working directory. To evaluate a trained model on other datasets, use the following command:

python src/eval.py experiment=<model config> data=<data config> ckpt_path=<ckpt path>

Then, you can use the scripts in amrlib to call BLINK to generate AMR graphs with wiki tags and compute the fine-grained metrics. The scripts could be found at https://github.com/bjascob/amrlib/blob/master/scripts/33_Model_Parse_XFM/.

This code is released under the MIT license.

If you use this work for research, please cite our paper:

@inproceedings{
lou2023amr,
title={{AMR} Parsing with Causal Hierarchical Attention and Pointers},
author={Chao Lou and Kewei Tu},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=SI2CXa5eok}
}