Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning.

Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning.

This repo contains the PyTorch implementaion for the paper Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning.

For any questions about the implementation, plaese email [email protected] or [email protected].

Model Overview

Requirement

• pytorch >= 0.4.0
• numpy >= 1.13.3
• sklearn
• python 3.6 / 3.7
• CUDA 9.0
• transformers

To install requirements, run pip install -r requirements.txt.

Dataset

you can directly use the processed dataset located in datasets/:
Note that you need to extract the data from the datasets folder: unzip datasets.zip

├── data
│   │   ├── semeval14(res14，laptop14)
│   │   ├── semeval15(res15)
│   │   ├── semeval16(res16)
│   │   ├── MAMS

The dataSet contains with cl_2X3 is the dataSet obtained after label argment, and each data is as follows:
Context
Aspect
Aspect-sentiment-label(-1:negative;0:netrual;1:positive)
Contrastive-label(aspect-dependent/aspect-invariant)
Contrastive-aspect-label(0:negative;1:netrual;2:positive)

Preparation

a) Download the pytorch version pre-trained bert-base-uncased model and vocabulary from the link provided by huggingface. Then change the value of parameter --bert_model_dir to the directory of the bert model. you can get the pre-trained bert-base-uncased model in https://github.com/huggingface/transformers.

b) Label enhancement method. For new data, additional supervised signals need to be obtained through label enhancement;
    i) Through BERT overfitting the training set, the acc can reach more than 97%;
    ii) Replace aspect with other or mask, and get the emotional label of the aspect after replacing the aspect;
    iii) Determine whether the output label is consistent with the real label, and fill in the aspect-dependent/aspect-invariant label for the data.

c) The data defaults are in data_utils.py, which you can view if you want to change the data entered into the model.

Training

1. Adjust the parameters and set the experiment.
   --model:Selection model.(bert_spc_cl)
   --dataset:Select dataSet.(acl14,res14,laptop14,res15,res16,mams and so on)
   --num_epoch：Iterations of the model.
   --is_test 0:Verify module.(1 is data verification, 0 is model training)
   --type: Select a task type.(normal,cl2,cl6,cl2X3)
2. Run the shell script to start the program.

bash run.sh

For run.sh code:

CUDA_VISIBLE_DEVICES=3 \
  python train_cl.py \
  --model_name bert_spc_cl \
  --dataset cl_mams_2X3 \
  --num_epoch 50 \
  --is_test 0 \
  --type cl2X3

For dataset,you can choose these dataset : "cl_acl2014_2X3" "cl_res2014_2X3" "cl_laptop2014_2X3" "cl_res2015_2X3" "cl_res2016_2X3" "cl_mams_2X3".

Testing

bash run_test.sh

Citation

@inproceedings{10.1145/3459637.3482096,
author = {Liang, Bin and Luo, Wangda and Li, Xiang and Gui, Lin and Yang, Min and Yu, Xiaoqi and Xu, Ruifeng},
title = {Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning},
year = {2021},
isbn = {9781450384469},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3459637.3482096},
doi = {10.1145/3459637.3482096},

Credits

The code of this repository partly relies on ABSA-PyTorch.