llj110 / RoBERTaABSA

Implementation of paper Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa.

RoBERTaABSA

This repo contains code for NAACL 2021 paper titled Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa. Our work focuses on the Aspect-level Sentiment Classification subtask and achieves a new state-of-the-art (SOTA) result.

You can find more information here:

• Paper
• Code
• Paperwithcode
• Explainaboard

For any questions about the implementation, feel free to create an issue or email me via [e-mail].

For the solution to whole ABSA task, please have a look at our ACL 2021 paper A Unified Generative Framework for Aspect-Based Sentiment Analysis.

Dependencies

We recommend to create a virtual environment.

conda create -n absa 
conda activate absa

packages:

• python 3.7

• pytorch 1.5.1

• transformers 2.11.0

• fastNLP

  • pip install git+https://github.com/fastnlp/fastNLP@dev

• fitlog

  • pip install git+https://github.com/fastnlp/fitlog

All code runs on linux only. For ASGCN, PWCN, RGAT, please refer to their own repos.

Data

I have received several issues on the reproduction. After the code re-checking and some discussions, we conjecture that the problem may be caused by the different usage of RoBERTa Tokenizer and some pre-processing. We release the datasets in the Dataset folder.

Usage

• To get our SOTA results with RoBARTa in Paperwithcode, simply run the finetune.py in Train folder. Before the code running, make sure the --data_dir and --dataset arguments are filled in correct file path.

• Reproduce the experiments in our paper:

  1. Fine-tuning the model on ABSA datasets using the code from the Train folder, which will save the fine-tuned models.

  python finetune.py --data_dir /user/project/dataset/ --dataset Restaurant

  2. Generate the induced trees using the code from the Perturbed-Masking folder, which will output the input datasets for different models.

  python generate_matrix.py --model_path Bert --data_dir /user/project/dataset/ --dataset Restaurant

  • model_path can be either Bert/RoBERTa/xlmRoberta/xlmbert or the model path where the fine-tuned model is put. Generate input data for different model.

  • ASGCN Input Data:

  python generate_asgcn.py --layers 11

  • PWCN Input Data:

  python generate_pwcn.py --layers 11

  • RGAT Input Data:

  python generate_rgat.py --layers 11

  3. Run the code in ASGCN, PWCN and RGAT.

Disclaimer

We made necessary changes based on the original code. We believe all the changes are under the MIT License permission. And we opensource all the changes we have made.

Notes

We notice that the learning rate in the paper got mistakes. Please refer to the learning rate in code, which is 2e-5 for RoBERTa.