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

Last update: Jan 03, 2023

Overview

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

To get the tokenized bleu score, you need to use the scorer we provide here. We use this script in order to ensure comparability with previous approaches.

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"
}

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

Related tags

Overview

AMRBART

Requirements

Data Processing

Pre-training

Fine-tuning

Evaluation

Inference on your own data

Pre-trained Models

Pre-trained AMRBART

Fine-tuned models on AMR-to-Text Generation

Fine-tuned models on AMR Parsing

Todo

References

Owner

xfbai

Minimal But Practical Image Classifier Pipline Using Pytorch, Finetune on ResNet18, Got 99% Accuracy on Own Small Datasets.

A simple rest api that classifies pneumonia infection weather it is Normal, Pneumonia Virus or Pneumonia Bacteria from a chest-x-ray image.

A selection of State Of The Art research papers (and code) on human locomotion (pose + trajectory) prediction (forecasting)

This repository contains the segmentation user interface from the OpenSurfaces project, extracted as a lightweight tool

ICCV2021, Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

A Python library for Deep Probabilistic Modeling

PAIRED in PyTorch 🔥

Luminaire is a python package that provides ML driven solutions for monitoring time series data.

TensorFlow implementation of "Attention is all you need (Transformer)"

PyTorch version repo for CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

Generate images from texts. In Russian. In PaddlePaddle

FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack

Learning to Predict Gradients for Semi-Supervised Continual Learning

Network Compression via Central Filter

A fast Evolution Strategy implementation in Python

SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning

Accurate Phylogenetic Inference with Symmetry-Preserving Neural Networks

以孤立语假设和宽度优先搜索为基础，构建了一种多通道堆叠注意力Transformer结构的斗地主ai

A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.

Official PyTorch implementation of MX-Font (Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts)