Source code for paper "ATP: AMRize Than Parse! Enhancing AMR Parsing with PseudoAMRs" @NAACL-2022

Chen Liang

Last update: Nov 23, 2022

Related tags

Deep Learning ATP-AMR

Overview

ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs

Hi this is the source code of our paper "ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs" accepted by findings of NAACL 2022.

News

🎈 Release camera ready paper. arXiv 2022.04.20
🎈 We have released four trained models and the test scripts. 2022.04.10

Todos

🎯 We are working on merging our training/preprocessing code with the amrlib repo.

Brief Introduction

TL;DR: SOTA AMR Parsing single model using only 40k extra data. Rank 1st model on Structrual-Related Scores (SRL and Reentrancy).

As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enhance AMR parsing. With carefully designed control experiments, we find that 1) Semantic role labeling (SRL) and dependency parsing (DP), would bring much more significant performance gain than unrelated tasks in the text-to-AMR transition. 2) To make a better fit for AMR, data from auxiliary tasks should be properly ``AMRized'' to PseudoAMR before training. 3) Intermediate-task training paradigm outperforms multitask learning when introducing auxiliary tasks to AMR parsing.

From an empirical perspective, we propose a principled method to choose, reform, and train auxiliary tasks to boost AMR parsing. Extensive experiments show that our method achieves new state-of-the-art performance on in-distribution, out-of-distribution, low-resources benchmarks of AMR parsing.

Requriments

Build envrionment for Spring

cd spring
conda create -n spring python=3.7
pip install -r requirements.txt
pip install -e .
# we use torch==1.11.0 and A40 GPU. lower torch version is fine.

Build envrionment for BLINK to do entity linking, Note that BLINK has some requirements conflicts with Spring, while the blinking script relies on both repos. So we build it upon Spring.

conda create -n blink37 -y python=3.7 && conda activate blink37

cd spring
pip install -r requirements.txt
pip install -e .

cd ../BLINK
pip install -r requirements.txt
pip install -e .
bash download_blink_models.sh

Preprocess and AMRization

coming soon ~

Training

(cleaning code and data in progress)

cd spring/bin

Train ATP-DP Task

python train.py --direction dp --config /home/cl/AMR_Multitask_Inter/spring/configs/config_dp.yaml

Train ATP-SRL Task

python train.py --direction dp --config /home/cl/AMR_Multitask_Inter/spring/configs/config_srl.yaml 
# yes, the direction is also dp

Train AMR Task based on intermediate ATP-SRL/DP Model

python train.py --direction amr --checkpoint PATH_TO_SRL_DP_MODEL --config ../configs/config.yaml

Train AMR,SRL,DP Task in multitask Manner

python train.py --direction multi --config ../configs/config_multitask.yaml

Inference

conda activate spring

cd script
bash intermediate_eval.sh MODEL_PATH 
# it will generate the gold and the parsed amr files, you should the change the path of AMR2.0/3.0 Dataset in the script.

conda activate blink37 
# you should download the blink models according to the ATP/BLINK/download_blink_models.sh in BLINK repo
bash blink.sh PARSED_AMR BLINK_MODEL_DIR

cd ../amr-evaluation
bash evaluation.sh PARSED_AMR.blink GOLD_AMR_PATH

Models Release

You could refer to the inference section and download the models below to reproduce the result in our paper.

ATP_SRL_AMR2.0 Google Drive

#scores
Smatch -> P: 0.858, R: 0.844, F: 0.851
Unlabeled -> P: 0.890, R: 0.874, F: 0.882
No WSD -> -> P: 0.863, R: 0.848, F: 0.855
Concepts -> P: 0.914 , R: 0.895 , F: 0.904
Named Ent. -> P: 0.928 , R: 0.901 , F: 0.914
Negations -> P: 0.756 , R: 0.758 , F: 0.757
Wikification -> P: 0.849 , R: 0.824 , F: 0.836
Reentrancies -> P: 0.756 , R: 0.744 , F: 0.750
SRL -> P: 0.840 , R: 0.830 , F: 0.835

ATP_SRL_D_AMR2.0 Google Drive

#scores
Smatch -> P: 0.859, R: 0.844, F: 0.852
Unlabeled -> P: 0.891, R: 0.876, F: 0.883
No WSD -> -> P: 0.863, R: 0.849, F: 0.856
Concepts -> P: 0.917 , R: 0.898 , F: 0.907
Named Ent. -> P: 0.942 , R: 0.921 , F: 0.931
Negations -> P: 0.742 , R: 0.755 , F: 0.749
Wikification -> P: 0.851 , R: 0.833 , F: 0.842
Reentrancies -> P: 0.753 , R: 0.741 , F: 0.747
SRL -> P: 0.837 , R: 0.830 , F: 0.833

ATP_SRL_D_AMR2.0_Ensemble Google Drive

#scores
Smatch -> P: 0.859, R: 0.847, F: 0.853
Unlabeled -> P: 0.891, R: 0.877, F: 0.884
No WSD -> -> P: 0.863, R: 0.851, F: 0.857
Concepts -> P: 0.917 , R: 0.899 , F: 0.908
Named Ent. -> P: 0.938 , R: 0.917 , F: 0.927
Negations -> P: 0.740 , R: 0.755 , F: 0.747
Wikification -> P: 0.849 , R: 0.830 , F: 0.840
Reentrancies -> P: 0.755 , R: 0.748 , F: 0.751
SRL -> P: 0.837 , R: 0.836 , F: 0.836

ATP_SRL_AMR3.0_Ensemble Google Drive

#scores
Smatch -> P: 0.844, R: 0.836, F: 0.840
Unlabeled -> P: 0.875, R: 0.866, F: 0.871
No WSD -> -> P: 0.849, R: 0.840, F: 0.845
Concepts -> P: 0.908 , R: 0.892 , F: 0.900
Named Ent. -> P: 0.900 , R: 0.879 , F: 0.889
Negations -> P: 0.734 , R: 0.729 , F: 0.731
Wikification -> P: 0.816 , R: 0.798 , F: 0.807
Reentrancies -> P: 0.729 , R: 0.749 , F: 0.739
SRL -> P: 0.822 , R: 0.830 , F: 0.826

Acknowledgements

We thank all people/group that share open-source scripts for this project, which include the authors for SPRING, amrlib, smatch, amr-evaluation, BLINK and all other repos.

Citation

If you feel our work helpful, please kindly cite

@misc{https://doi.org/10.48550/arxiv.2204.08875,
  doi = {10.48550/ARXIV.2204.08875},
  
  url = {https://arxiv.org/abs/2204.08875},
  
  author = {Chen, Liang and Wang, Peiyi and Xu, Runxin and Liu, Tianyu and Sui, Zhifang and Chang, Baobao},
  
  keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}

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Comments

shape size mismatch

Hi, I'm trying your new model to reproduce the result, but your released model's shape is different from the released code.

I downloaded all of your checkpoints, but shell script shows these error messages as follows.

RuntimeError: Error(s) in loading state_dict for AMRBartForConditionalGeneration: size mismatch for final_logits_bias: copying a param with shape torch.Size([1, 53631]) from checkpoint, the shape in current model is torch.Size([1, 53587]). size mismatch for model.shared.weight: copying a param with shape torch.Size([53631, 1024]) from checkpoint, the shape in current model is torch.Size([53587, 1024]). size mismatch for model.encoder.embed_tokens.weight: copying a param with shape torch.Size([53631, 1024]) from checkpoint, the shape in current model is torch.Size([53587, 1024]). size mismatch for model.decoder.embed_tokens.weight: copying a param with shape torch.Size([53631, 1024]) from checkpoint, the shape in current model is torch.Size([53587, 1024]).
good first issue

opened by wolyosung 5

Source code for paper "ATP: AMRize Than Parse! Enhancing AMR Parsing with PseudoAMRs" @NAACL-2022

Related tags

Overview

ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs

News

Todos

Brief Introduction

Requriments

Preprocess and AMRization

Training

Inference

Models Release

Acknowledgements

Citation

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