FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning

This repository contains the implementation of our EACL 2023 paper "FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning" (https://arxiv.org/abs/2302.04834). FrameBERT is a BERT-based model that leverages FrameNet embeddings for improved metaphor detection and model explainability. Our extensive experiments demonstrate the effectiveness of FrameBERT on four public benchmark datasets (VUA, MOH-X, TroFi) compared to the base model and state-of-the-art models.

Important updates: I have just added a inference.py to enable quick metaphor and frame detection on your customized data. I plan to add more features in the future. So please star our project to get posted.

0. To Start:

1. Clone the repository:

git clone https://github.com/liyucheng09/MetaphorFrame.git
cd MetaphorFrame

2. Install the required packages:

pip install -r requirements.txt

1. Run FrameBERT on Your data:

3. If you just want to run FrameBERT directly on your own data, just run:

python inference.py example_articles.json

Put your own data in example_articles.json. Check out example_articles.json and inference.py, you can easily edit them to run the program on large amount of articles.

This will produce the results to a predictions.tsv, which look like this:

Tokens	Borderline_metaphor	Real_metaphors	Frame_label
The	0	0	_
Frozen	1	1	_
Political	0	0	_
Battlefield	1	1	_
In	1	0	_
fact	0	0	_
,	0	0	_
in	1	0	_
normal	0	0	Typicality
circumstances	0	0	_
,	0	0	_
the	0	0	_
incumbent	0	0	_
would	0	0	_
look	1	0	Give_impression

The column Borderline_metaphor indicates a wide range of metaphor which can be very conventional, but Real_metaphor represents more interesting and novel metaphors. The Frame_label represents the identified Frame labels.

Citation

If you find this repository helpful for your research, please cite our paper:

@misc{li2023framebert,
      title={FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning}, 
      author={Yucheng Li and Shun Wang and Chenghua Lin and Frank Guerin and Loïc Barrault},
      year={2023},
      eprint={2302.04834},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

For any questions or issues, please feel free to open an issue on GitHub or contact the authors directly.

Reproduce the paper (optional)

You don't have to reproduce the results in the paper, if you just want to use a metaphor detection tool.

But if you want to reproduce FrameBERT from scratch:

3. Unzip the data:

unzip data_all.zip

After unzipping, the frame data can be found at data_all/open_sesame_v1_data, and other data such as VUA, MOH, and TroFi datasets can be found in their respective directories.

4. Prepare the frame_finder model first before we run the entire framewrok. Traning the frame model will take around 2 hours.

./scripts/ff.sh

5. config data path and frame_finder path in main_config.cfg

6. Run the main script, training on VUA18 will take about 5 hours:

./scripts/run.sh

To see the meaning of all variables, check the explaination in the config file main_config.cfg.

Repository Structure

The repository is organized as follows:

• scripts/: Contains all bash scripts with relevant code execution and arguments for each script.
  • scripts/run.sh: The main script for running FrameBERT.
• main_config.cfg: Configuration file for main.py.
• data_all.zip: Compressed file containing all the data needed for the project.
• frame_finder/: Directory containing the frame embedding model.
• requirements.txt: Lists the required packages for the project.

Configuration

You can modify the configuration of the FrameBERT model by editing the main_config.cfg file. This file contains various settings and hyperparameters for the model.