Code for NAACL 2019 paper: Adversarial Category Alignment Network for Cross-domain Sentiment Classification
Code for NAACL 2019 paper: “Adversarial Category Alignment Network for Cross-domain Sentiment Classification” (pdf)
You can download the datasets (amazon-benchmark) at [Download]. The zip file should be decompressed and put in the root directory.
Download the pretrained Glove vectors [glove.840B.300d.zip]. Decompress the zip file and put the txt file in the root directory.
You can find arguments and hyper-parameters defined in train_batch.py with default values.
Under code/, use the following command for training any source-target pair from the amazon benchmark:
CUDA_VISIBLE_DEVICES="0" python train_batchs.py \--emb ../glove.840B.300d.txt \--dataset amazon \--source $source \--target $target \--n-class 2 \--lamda1 -0.1 --lamda2 0.1 --lamda3 5 --lamda4 1.5 \--epochs 30
where —emb is the path to the pre-trained word embeddings. $source and $target are domains from the amazon benchmark, both in [‘book’, ‘dvd’, ‘electronics’, ‘kitchen’]. —n-class denoting the number of output classes is set to 2 as we only consider binary classification (positive or negative) on this dataset. All other hyper-parameters are left as their defaults.
The code was only tested under the environment below:
If you use the code, please cite the following paper:
@InProceedings{qu-etal-2019-adversarial,author = {Qu, Xiaoye and Zou, Zhikang and Cheng, Yu and Yang, Yang and Zhou, Pan},title = {Adversarial Category Alignment Network for Cross-domain Sentiment Classification},booktitle = {Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics},publisher = {Association for Computational Linguistics}}