Cross-lingual AMR aligner: Paying Attention to Cross-Attention

This is the repo for Cross-lingual AMR Aligner, a novel aligner for Abstract Meaning Representation (AMR) graphs that can scale cross-lingually, presented at ACL 2023.

Features

This repository presents an extended version of the SPRING model specifically designed for cross-lingual alignment of Abstract Meaning Representation (AMR) graphs. Our enhanced aligner incorporates two key features:

1. Cross-Attention Aligner: We introduce a novel module that leverages the Cross-Attention mechanism to extract alignment information.

2. Guided Attention model: We augment the training phase with an extra loss-signal based on the alignment information, improving the model's ability to capture alignment patterns in the Cross-Attention.

If you use the code, please reference this work in your paper:

@inproceedings{martinez-etal-2023-amr-aligner,
    title = {{C}ross-lingual {AMR} {A}ligner: {P}aying {A}ttention to {C}ross-{A}ttention},
    author = {Martínez Lorenzo, Abelardo Carlos and Huguet Cabot, Pere-Lluís and Navigli, Roberto},
    booktitle = {Findings of ACL},
    year = {2023}
}

Contents

The repository is organized as follows:

• bin/: Contains the source code for training the SPRING model, conducting inference, and extracting perplexity.
• config/: Contains configuration files for training the SPRING model.
• data/: should contain the training, validation, and test corpora, along with the necessary vocab of SPRING.
• spring_amr/: Contains the SPRING model code.
• docs/: Documentation and supplementary material related to the project.

Pretrained Checkpoints

Text-to-AMR Parsing

• Model (unguided) trained in the AMR 3.0 training set: AMR3.parsing-1.0.tar.bz2

If you need the checkpoints of other experiments in the paper, please send us an email.

Installation

cd amr-alignment
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.

Extract alignments

To extract the aligments from the cross-attention of a AMR parsing SPRING-like model (either guided or unguided), you can use the following command:

python bin/extract_alignment.py --checkpoint <path_to_checkpoint> --amr-path <path_to_amr_file>

This will generate multiple files in LeAMR and ISI formats.

To evaluate them, please refer to the evaluation code from LeAMR.

License

This project is released under the CC-BY-NC-SA 4.0 license (see LICENSE). If you use Cross-lingual AMR Aligner, please reference the paper and put a link to this repo.

Contributing

We welcome contributions to the Cross-lingual AMR Aligner project. If you have any ideas, bug fixes, or improvements, feel free to open an issue or submit a pull request.

Contact

For any questions or inquiries, please contact Abelardo Carlos Martínez Lorenzo at [email protected] or Pere-Lluís Huguet Cabot at [email protected]