Dataset and source code for our paper: "EATN: An Efficient Adaptive Transfer Network for Aspect-level Sentiment Analysis".
— Python 3.6
— Numpy 1.13.3
— Transformer
— sklearn
— other pakages
To install requirements, please run pip install -r requirements.txt.
— OS: CentOS Linux release 7.7.1908
— CPU: 64 Intel(R) Xeon(R) Gold 5218 CPU @ 2.30GHz
— GPU: Four Tesla V100-SXM2 32GB
— CUDA: 10.2
Prepare the Pre-trained model :
— 1. Get the BERT pre-trained model and generate the embeddings (./word2vec/get_pre_bert.sh) ;
— You can get the Word Embeddings through official BERT or Bert-As-Service ;
— Google Word2Vec ;
— GloVe ;
— 2. Put the pre-trained model (Google-Word2Vec/Bert) to the coresponseding path ;
Run the baseline models :
— python train_base.py --model_name xxx --dataset xxx
Run the eatn models :
— python train_eatn.py （*Default transfer task is Laptop-2-Restaurant / L2R; The parameters can be changed in the .py file!
If you have any problem about this library, please create an Issue or send us an Email at:
— kkzhang0808@mail.ustc.edu.cn
If the data and code are useful for your research, please be kindly to give us stars and cite our paper as follows:
@ARTICLE{9415156,
author={Zhang, Kai and Liu, Qi and Qian, Hao and Xiang, Biao and Cui, Qing and Zhou, Jun and Chen, Enhong},
journal={IEEE Transactions on Knowledge and Data Engineering},
title={EATN: An Efficient Adaptive Transfer Network for Aspect-level Sentiment Analysis},
year={2021},
volume={},
number={},
pages={1-1},
doi={10.1109/TKDE.2021.3075238}}