README.md

MAGIC

Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation, ACL 2024 (main)

This is the official repo for our paper Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation

Model Overview

An overview of MAGIC. Top part: We use the prefix tuning-based autoencoder structure as the framework and construct the attribute latent space. Bottom left: The vectors with counterfactual attribute features generated by the attribute disentanglement module are assisted in the construction of the attribute latent space. Bottom right Inference stage with target-guided attribute correlation augmentation to improve multi-aspect control.

Dataset

1. Download our training data from this link. Unzip training data and put them under the data directory.

2. Download the discriminator checkpoints (Discriminator, used to evaluate multi-aspect control. Unzip them and put them under the model folder.

Quick Start

1. Training of MAGIC.

python train_multi.py 
--model_dir ./model \
--Ag_News_Path ./data/AG-data-7_3_sentiment.txt \
--IMDB_Path ./data/IMDb.txt \
--Toxic_Path ./data/Toxic.txt \
--batch_size 64 \
--epoch 300 \
--max_length 100 \
--sparse_loss 0.4 \
--latent_classify_loss 0.2 \
--aspect_gap_loss 0.3

2. Generation and Evaluation. Conduct conditional generation to obtain desired multi-apsect senteces. After generation, perform automatic evaluation to assess the quality of generated texts.

python probe.py 
--model_path ./model/CKPT_NAME \
--model_prefix add_flip \
--Agnews_path ./data/AG-data-7_3_sentiment.txt \
--output_dir ./generated_txt \
--log_res_root_path ./probe_eval_log \
--property default

Reference

If you find the code helpful, please cite our paper:

@article{liu2024multi,
  title={Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation},
  author={Liu, Yi and Liu, Xiangyu and Zhu, Xiangrong and Hu, Wei},
  journal={arXiv preprint arXiv:2405.19958},
  year={2024}
}