MiniConGTS

Overview

This repository serves as the official codebase for my recent work, "MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction". The project implements a minimalist tagging scheme and a novel token-level contrastive learning strategy to enhance aspect sentiment triplet extraction performance. The approach leverages the power of Pretrained Language Models (PLMs), such as BERT, RoBERTa etc. to achieve state-of-the-art results without relying on complex classification head designs or external semantic enhancements.

Arxiv Preprint

Key Features

Minimalist Grid Tagging Scheme: Uses the fewest classes of labels to simplify the tagging process.
Token-level Contrastive Learning: Improves the representation quality of PLMs, enhancing their internal potential.
High Performance: Achieves state-of-the-art results in Aspect-Based Sentiment Analysis (ABSA) with minimal reliance on complex architectures.
Evaluation on GPT Models: Includes first-time evaluations of the Chain-of-Thought method in context learning scenarios.
Comprehensive Analysis: Provides proofs and theoretical analyses to support the efficacy of the proposed methods.

Repository Structure

MiniConGTS/
│
├── data/                   # Directory for datasets
│   ├── D1/
│   │   ├── res14/
│   │   │   ├── train.json
│   │   │   ├── dev.json
│   │   │   └── test.json
│   │   └── ...             # Other datasets
│   └── D2/
│       └── ...
│
├── modules/
│   ├── models/
│   │   ├── roberta.py      # RoBERTa model definition
│   │   └── saved_models/   # Directory for saving trained models
│   └── f_loss.py           # Focal loss implementation
│
├── tools/
│   └── trainer.py          # Training and evaluation script
│
├── utils/
│   ├── common_utils.py     # Utility functions
│   ├── data_utils.py       # Data loading and preprocessing
│   └── ...
│
├── main.py                 # Main script for training and evaluation
├── requirements.txt        # List of required packages
└── README.md               # This file

Getting Started

Installation

Clone the repository:

git clone https://github.com/your-repo/MiniConGTS.git
cd MiniConGTS

Install dependencies:
```
pip install -r requirements.txt
```

Preparing the Data

Place your datasets in the ./data/ directory following the provided structure.

Training the Model

Configure training parameters in the main.py script or pass them as command-line arguments.

Run the training script:

python main.py --max_sequence_len 100 --batch_size 16 --epochs 2000 --dataset res14

for more parameter setting:

python main.py \
 --max_sequence_len 100 \
 --sentiment2id "{'negative': 2, 'neutral': 3, 'positive': 4}" \
 --model_cache_dir "./modules/models/" \
 --model_name_or_path "roberta-base" \
 --batch_size 16 \
 --device "cuda" \
 --prefix "./data/" \
 --data_version "D1" \
 --dataset "res14" \
 --bert_feature_dim 768 \
 --epochs 2000 \
 --class_num 5 \
 --task "triplet" \
 --model_save_dir "./modules/models/saved_models/" \
 --log_path "./logs/training_log.log" \
 --learning_rate 1e-3 \
 --warmup_steps 500 \
 --weight_decay 0.01

Alternatively, you can start your jupyter kernal and debug each intermidiate step easily in a notebook using:

main.ipynb

Evaluation

The model is evaluated on the test set after training. The results, including metrics such as accuracy, precision, recall, and F1-score, are logged to a file specified in the log_path.

Citation

If you use this code in your research, please cite the paper as follows:

@article{yourpaper2024,
  title={MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction},
  author={Your Name},
  journal={Journal Name},
  year={2024}
}

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

Special thanks to the authors and contributors of the transformers library by Hugging Face.

qiaosun22/MiniConGTS