CLAP: Compact Linearization with an Adaptable Parser

Welcome to the official repository for CLAP, an innovative architecture for AMR (Abstract Meaning Representation) parsing, presented at LREC-COLING 2024.

Features

1. AMR Parsing and Generation: CLAP introduces a flexible and efficient AMR parsing architecture. It supports seamless transitions between different language models and facilitates multilingual adaptability.

2. Crosslingual AMR Alignment: Integration of the Crosslingual AMR Aligner enables extraction of span-to-node alignments from sentences to graphs, leveraging the model's cross-attention capabilities.

3. Perplexity Extraction: Incorporating the AMRs Assemble, CLAP can compute perplexity scores and supports training in assembly tasks.

Citing This Work

If you use CLAP in your research, please cite our paper:

@inproceedings{martinez-lorenzo-navigli-2024-efficient-amr,
    title = "Efficient {AMR} Parsing with {CLAP}: Compact Linearization with an Adaptable Parser",
    author = "Martinez Lorenzo, Abelardo Carlos and Navigli, Roberto",
    editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.495",
    pages = "5578--5584",
}

Repository Structure

• conf/: Configuration files for data paths, model specifications, and training parameters.
• data/: Datasets for benchmarking AMR evaluation.
• experiments/: Stores checkpoints post-training.
• models/: Trained Hugging Face models.
• src/: Source code for the project.
  • constant.py: Manages tokens added to the model; customizable for new tokens.
  • linearization.py: Implements graph linearization in Depth-First Search and compact formats.
  • pl_data_modules.py: Data module classes for training.
  • pl_modules.py: Contains new modular components for the architecture.
  • predict.py: Script for making predictions using trained models.
  • predict_alignment.py: Script for extracting alignments.
  • predict_perplexity.py: Script for computing perplexity.
  • train.py: Entry point for training models.
  • utils.py: Utility functions for various operations.

Installation

# Create a Python 3.9 environment
conda create -n clap-env python=3.9
conda activate clap-env

# Install dependencies
pip install -r requirements.txt

Training

Configure paths and hyperparameters in conf/ directory files:

• conf/data.yaml: Specify dataset paths for training and evaluation.
• conf/model.yaml: Define the model architecture, e.g., google/flan-t5-small.
• conf/train.yaml: Adjust training-specific hyperparameters.

python src/train.py

Prediction

Set up the necessary paths in conf/data.yaml and conf/model.yaml. Then run:

python src/predict.py

Alignment Extraction

Configure as per the prediction step and execute:

python src/predict_alignments.py

Perplexity Calculation

Configure as per the prediction step and execute:

python src/predict_perplexity.py

License

This project is released under the CC-BY-NC-SA 4.0 license (see LICENSE). If you use AMRs-Assemble!, 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 martineslorenzo@diag.uniroma.it