Transferable-E2E-ABSA

Data and source code for our EMNLP'19 Long paper, oral, "Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning".

Update:

Oct 6th, 2019: The experimental code (not fully clean version) has been released.

Oct 31th, 2019: The paper has been released in the arkiv.

Introduction

1) E2E-ABSA: This task aims to jointly learn aspects as well as their sentiments from user reviews, whch can be effectively formulated as an end-to-end sequence labeling problem based on the unified tagging scheme.

The unified tagging is similar to the NER tagging.

unified tag = aspect boundary tag + sentiment tag

NER tag = entity boundary tag + entity type tag

As we all know, labeling sequence data behaves much more expensive and time-comsuming.

2) Transferable-E2E-ABSA: we firstly explore an unsupervised domain adaptation (UDA) setting for cross-domain E2E-ABSA. Unlike the traditional UDA in classification problems, this task aims to leverage knowledge from a labeled source domain to improve the sequence learning in an unlabeled target domain.

Requirements

• Python 2.7.12

• Tensorflow-gpu 1.4.1

• numpy 1.15.4

Environment

• OS: CentOS Linux release 7.5.1804
• GPU: NVIDIA TITAN Xp
• CUDA: 8.0

Running

Download (Password: zlyc) the word embedding and then move it to the data directory. The embedding is pre-trained on Yelp and Electronics dataset.

AD-SAL (full Model):

selective adversairal learning on the low-level AD task.

python main.py --train --test -s rest -t service -model_name AD-SAL --selective

AD-AL (ablation Model):

pure adversairal learning without selectivity on the low-level AD task.

python main.py --train --test -s rest -t service -model_name AD-AL

Training over all transfer pairs:

./scripts/train_AD-AL.sh
./scripts/train_AD-SAL.sh

Citation

If the source code and data are useful for your research, please be kindly to give us stars and cite our paper as follows:

@article{li2019sal,
  title={Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning},
  author={Li, Zheng and Li, Xin and Wei Ying and Bing Lidong and Zhang Yu and Yang, Qiang},
  conference={EMNLP},
  year={2019}
}