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

A new play-and-plug method of controlling an existing generative model with conditioning attributes and their compositions.

Automated detection of anomalous exoplanet transits in light curve data.

TensorFlow implementation of Adaptive Information Transfer Multi-task (AITM) framework. Code for the paper submitted to KDD21: Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning for Customer Acquisition.

[ICCV 2021] FaPN: Feature-aligned Pyramid Network for Dense Image Prediction

The PASS dataset: pretrained models and how to get the data - PASS: Pictures without humAns for Self-Supervised Pretraining

Visual Tracking by TridenAlign and Context Embedding

PyTorch implementation of GLOM

Official repository for the paper "Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks"

Code for the paper Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration

Dictionary Learning with Uniform Sparse Representations for Anomaly Detection

WRENCH: Weak supeRvision bENCHmark

Adversarially Learned Inference

A python implementation of Yolov5 to detect fire or smoke in the wild in Jetson Xavier nx and Jetson nano

某学校选课系统GIF验证码数据集 + Baseline模型 + 上下游相关工具

Multiple Object Extraction from Aerial Imagery with Convolutional Neural Networks

Volumetric parameterization of the placenta to a flattened template

Using CNN to mimic the driver based on training data from Torcs

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Implementation of the state-of-the-art vision transformers with tensorflow

Pytorch implementation of "Neural Wireframe Renderer: Learning Wireframe to Image Translations"