This is the repo for SPRING (Symmetric ParsIng aNd Generation), a novel approach to semantic parsing and generation, presented at AAAI 2021.
With SPRING you can perform both state-of-the-art Text-to-AMR parsing and AMR-to-Text generation without many cumbersome external components. If you use the code, please reference this work in your paper:
@inproceedings{bevilacqua-etal-2021-one,
title = {One {SPRING} to Rule Them Both: {S}ymmetric {AMR} Semantic Parsing and Generation without a Complex Pipeline},
author = {Bevilacqua, Michele and Blloshmi, Rexhina and Navigli, Roberto},
booktitle = {Proceedings of AAAI},
year = {2021}
}
Here we release our best SPRING models which are based on the DFS linearization.
-
Model trained in the AMR 2.0 training set: AMR2.parsing-1.0.tar.bz2
-
Model trained in the AMR 3.0 training set: AMR3.parsing-1.0.tar.bz2
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Model trained in the AMR 2.0 training set: AMR2.generation-1.0.tar.bz2
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Model trained in the AMR 3.0 training set: AMR3.generation-1.0.tar.bz2
If you need the checkpoints of other experiments in the paper, please send us an email.
cd spring
pip install -r requirements.txt
pip install -e .The code only works with transformers < 3.0 because of a disrupting change in positional embeddings.
The code works fine with torch 1.5. We recommend the usage of a new conda env.
Modify config.yaml in configs. Instructions in comments within the file. Also see the appendix.
python bin/train.py --config configs/config.yaml --direction amrResults in runs/
python bin/train.py --config configs/config.yaml --direction textResults in runs/
python bin/predict_amrs.py \
--datasets <AMR-ROOT>/data/amrs/split/test/*.txt \
--gold-path data/tmp/amr2.0/gold.amr.txt \
--pred-path data/tmp/amr2.0/pred.amr.txt \
--checkpoint runs/<checkpoint>.pt \
--beam-size 5 \
--batch-size 500 \
--device cuda \
--penman-linearization --use-pointer-tokensgold.amr.txt and pred.amr.txt will contain, respectively, the concatenated gold and the predictions.
To reproduce our paper's results, you will also need need to run the BLINK
entity linking system on the prediction file (data/tmp/amr2.0/pred.amr.txt in the previous code snippet).
To do so, you will need to install BLINK, and download their models:
git clone https://github.com/facebookresearch/BLINK.git
cd BLINK
pip install -r requirements.txt
sh download_blink_models.sh
cd models
wget http://dl.fbaipublicfiles.com/BLINK//faiss_flat_index.pkl
cd ../..Then, you will be able to launch the blinkify.py script:
python bin/blinkify.py \
--datasets data/tmp/amr2.0/pred.amr.txt \
--out data/tmp/amr2.0/pred.amr.blinkified.txt \
--device cuda \
--blink-models-dir BLINK/modelsTo have comparable Smatch scores you will also need to use the scripts available at https://github.com/mdtux89/amr-evaluation, which provide
results that are around ~0.3 Smatch points lower than those returned by bin/predict_amrs.py.
python bin/predict_sentences.py \
--datasets <AMR-ROOT>/data/amrs/split/test/*.txt \
--gold-path data/tmp/amr2.0/gold.text.txt \
--pred-path data/tmp/amr2.0/pred.text.txt \
--checkpoint runs/<checkpoint>.pt \
--beam-size 5 \
--batch-size 500 \
--device cuda \
--penman-linearization --use-pointer-tokensgold.text.txt and pred.text.txt will contain, respectively, the concatenated gold and the predictions.
For BLEU, chrF++, and Meteor in order to be comparable you will need to tokenize both gold and predictions using JAMR tokenizer.
To compute BLEU and chrF++, please use bin/eval_bleu.py. For METEOR, use https://www.cs.cmu.edu/~alavie/METEOR/ .
For BLEURT don't use tokenization and run the eval with https://github.com/google-research/bleurt. Also see the appendix.
The previously shown commands assume the use of the DFS-based linearization. To use BFS or PENMAN decomment the relevant lines in configs/config.yaml (for training). As for the evaluation scripts, substitute the --penman-linearization --use-pointer-tokens line with --use-pointer-tokens for BFS or with --penman-linearization for PENMAN.
This project is released under the CC-BY-NC-SA 4.0 license (see LICENSE). If you use SPRING, please put a link to this repo.
The authors gratefully acknowledge the support of the ERC Consolidator Grant MOUSSE No. 726487 and the ELEXIS project No. 731015 under the European Union’s Horizon 2020 research and innovation programme.
This work was supported in part by the MIUR under the grant "Dipartimenti di eccellenza 2018-2022" of the Department of Computer Science of the Sapienza University of Rome.
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