Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation

The code and dataset for our ACL'23 paper: Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation

The paper proposes the novel task of intent-specific counterspeech generation. We develop IntentCONAN, a diverse intent-specific counterspeech dataset by augmenting the existing CONAN dataset. We propose QUARC, a two-stage framework for generating intent-conditioned counterspeeches by fusing the learned vector-quantized representations of each intent category.

Repository Structure

The structure of this repository is as follows:

Folders

configs/: contains the YAML configuration files for the model parameters (for fast prototyping).
data/: Contains the training, validation and testing datasets.
modeling/: contains the model architectures and utilities.
models/: folder to store the trained models
utils/: contains various utilities used for training and testing

Files

process_data_clime.py: contains data processing for clime
process_data_cogent.py: contains data processing for cogent
train_clime.py: script for training clime
train_cogent.py: script for training cogent
results.py: contains the evaluation script to obtain the results
requirements.txt: the required libraries with their versions

Running the code

To run the code, first install the necessary requirements via the following command:
```
pip install requirements.txt
```
Then, set the hyperparamters by creating a configuration file in the config folder. A sample configuration file is provided, copy it and change the desired parameters.

Run CLIME by executing the following command:

python train_clime.py --config_path configs/<your_config>.yaml

Run COGENT by executing the following command:

python train_cogent.py --config_path configs/<your_config>.yaml

Contact