Fantabulous-J/AMRBART

Code for our paper "Graph Pre-training for AMR Parsing and Generation" in ACL2022

AMRBART

An implementation for ACL2022 paper "Graph Pre-training for AMR Parsing and Generation". You may find our paper here (Arxiv).

Requirements

• python 3.8
• pytorch 1.8
• transformers 4.8.2
• pytorch-lightning 1.5.0
• Tesla V100 or A100

We recommend to use conda to manage virtual environments:

conda env update --name <env> --file requirements.yml

We also provide a docker image here.

Data Processing

You may download the AMR corpora at LDC.

We follow Spring to preprocess AMR graphs:

# 1. install spring 
cd spring && pip install -e .
# 2. processing data
bash run-preprocess.sh

Pre-training

bash run-posttrain-bart-textinf-joint-denoising-6task-large-unified-V100.sh /path/to/BART/

Fine-tuning

For AMR Parsing, run

bash finetune_AMRbart_amrparsing.sh /path/to/pre-trained/AMRBART/ gpu_id

For AMR-to-text Generation, run

bash finetune_AMRbart_amr2text.sh /path/to/pre-trained/AMRBART/ gpu_id

Evaluation

For AMR Parsing, run

bash eval_AMRbart_amrparsing.sh /path/to/fine-tuned/AMRBART/ gpu_id

For AMR-to-text Generation, run

bash eval_AMRbart_amr2text.sh /path/to/fine-tuned/AMRBART/ gpu_id

Inference on your own data

If you want to run our code on your own data, try to transform your data into the format here, then run

For AMR Parsing, run

bash inference_amr.sh /path/to/fine-tuned/AMRBART/ gpu_id

For AMR-to-text Generation, run

bash inference_text.sh /path/to/fine-tuned/AMRBART/ gpu_id

Pre-trained Models

Pre-trained AMRBART

Setting	Params	checkpoint
AMRBART-base	142M	model
AMRBART-large	409M	model

Fine-tuned models on AMR-to-Text Generation

Setting	BLEU(tok)	BLEU(detok)	checkpoint	output
AMRBART-large (AMR2.0)	49.8	45.7	model	output
AMRBART-large (AMR3.0)	49.2	45.0	model	output

Fine-tuned models on AMR Parsing

Setting	Smatch	checkpoint	output
AMRBART-large (AMR2.0)	85.4	model	output
AMRBART-large (AMR3.0)	84.2	model	output

Todo

• clean code

References

@inproceedings{bai-etal-2022-graph,
    title = "Graph Pre-training for {AMR} Parsing and Generation",
    author = "Bai, Xuefeng  and
      Chen, Yulong and
      Zhang, Yue",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "todo",
    doi = "todo",
    pages = "todo"
}