Stochastic Adversarial Network for Multi-Domain Text Classification

Implementation of Stochastic Adversarial Network for Multi-Domain Text Classification in Pytorch

Datasets

We used the same dataset as Conditional Adversarial Networks for Multi-Domain Text Classification (CAN) .So, You can download from https://github.com/YuanWu3/Conditional-Adversarial-Networks-for-Multi-Domain-Text-Classification/tree/main/data Put the dataset into the corresponding folder: fdu-mtl、prep-amazon and w2v.

Requirements:

-Python 3.6 -Pytorch 1.10 -Torchnet -Scipy -Tqdm

Initialization:

To obtain the initial model, run and put it into the folder "./save/init_model":

Experiment 1: MDTC on the multi-domain Amazon dataset

cd code/
python exp1_init.py --dataset prep-amazon --model mlp 

Experiment 2: Multi-Source Domain Adaptation

cd code/
# target domain: books
python exp2_init.py --dataset prep-amazon --model mlp --no_wgan_trick --domains dvd electronics kitchen --unlabeled_domains books --dev_domains books
# target domain: dvd
python exp2_init.py --dataset prep-amazon --model mlp --no_wgan_trick --domains books electronics kitchen --unlabeled_domains dvd --dev_domains dvd
# target domain: electronics
python exp2_init.py --dataset prep-amazon --model mlp --no_wgan_trick --domains books dvd kitchen --unlabeled_domains electronics --dev_domains electronics
# target domain: kitchen
python exp2_init.py --dataset prep-amazon --model mlp --no_wgan_trick --domains dvd electronics kitchen --unlabeled_domains kitchen --dev_domains kitchen

Experiment 3: MDTC on the FDU-MTL dataset

cd code/
python exp3_init.py --dataset fdu-mtl --model cnn --max_epoch 30

Training

All the parameters are set as the same as parameters mentioned in the article. You can use the following commands to the tasks:

Experiment 1: MDTC on the multi-domain Amazon dataset

cd code/
python exp1_with_pseu_label.py --dataset prep-amazon --model mlp

Experiment 2: Multi-Source Domain Adaptation

cd code/
# target domain: books
python exp2_with_pseu_label.py --dataset prep-amazon --model mlp --no_wgan_trick --domains dvd electronics kitchen --unlabeled_domains books --dev_domains books
# target domain: dvd
python exp2_with_pseu_label.py --dataset prep-amazon --model mlp --no_wgan_trick --domains books electronics kitchen --unlabeled_domains dvd --dev_domains dvd
# target domain: electronics
python exp2_with_pseu_label.py --dataset prep-amazon --model mlp --no_wgan_trick --domains books dvd kitchen --unlabeled_domains electronics --dev_domains electronics
# target domain: kitchen
python exp2_with_pseu_label.py --dataset prep-amazon --model mlp --no_wgan_trick --domains dvd electronics kitchen --unlabeled_domains kitchen --dev_domains kitchen

Experiment 3: MDTC on the FDU-MTL dataset

cd code/
python exp3_with_pseu_label.py --dataset fdu-mtl --model cnn --max_epoch 30