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https://github.com/liyucheng09/metaphorframe
FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning. Presented at EACL 2023.
https://github.com/liyucheng09/metaphorframe
Last synced: 2 months ago
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FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning. Presented at EACL 2023.
- Host: GitHub
- URL: https://github.com/liyucheng09/metaphorframe
- Owner: liyucheng09
- Created: 2021-10-28T14:49:46.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-09-14T23:29:24.000Z (over 1 year ago)
- Last Synced: 2023-09-15T15:56:43.609Z (over 1 year ago)
- Language: Python
- Homepage:
- Size: 18.1 MB
- Stars: 10
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# 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.