Data and source code for our AAAI'18 paper "Hierarchical Attention Transfer Network for Cross-domain Sentiment Classification", which is an extension of our IJCAI'17 paper "End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification".
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Python 2.7.5
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Tensorflow-gpu 1.2.1
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numpy 1.13.3
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nltk 3.2.1
- OS: CentOS Linux release 7.5.1804
- GPU: NVIDIA TITAN Xp
- CUDA: 8.0
Before you get started, please make sure to add the following to your ~/.bashrc:
Linux:
export PYTHONPATH=/path/to/HATN:$PYTHONPATH
Centos:
setenv PYTHONPATH /path/to/HATN
The goal is to automatically capture pos/neg pivots as a bridge across domains based on PNet, which provides the inputs and labels for NPnet. If the pivots are already obtained, you can ignore this step.
python extract_pivots.py --train --test -s dvd [source_domain] -t electronics [target_domain] -v [verbose]
PNet and NPnet are jointly trained for cross-domain sentiment classification. When there exists large domain discrepany, it can demonstrate the efficacy of NPnet.
python train_hatn.py --train --test -s dvd [source_domain] -t electronics [target_domain] -v [verbose]
./all_train.sh
If the data and code are useful for your research, please cite our paper as follows:
@inproceedings{li2018hatn,
author = {Zheng Li and Ying Wei and Yu Zhang and Qiang Yang},
title = {Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification},
conference = {AAAI Conference on Artificial Intelligence},
year = {2018},
}
@inproceedings{li2017end,
title={End-to-end adversarial memory network for cross-domain sentiment classification},
author={Li, Zheng and Zhang, Yu and Wei, Ying and Wu, Yuxiang and Yang, Qiang},
booktitle={Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2017)},
year={2017}
}