This repo contains code for NAACL 2021 paper titled Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa.

The summarized information is here:

• Paper
• Code
• Paperwithcode
• Explainaboard

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

I have received some questions about the reproduction issues. After the re-check of the code 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.

To get our RoBERTa results, 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.

0. This section is for the reproduction of experiments in the original paper; for the reproduction of RoBERTa SOTA results in Paperwithcode, please refer to the next section.
1. Fine-tuning the model on ABSA datasets using the code from the Train folder, which will save the fine-tuned models.
2. Generate the induced trees using the code from the Perturbed-Masking folder, which will output the input datasets for different models.
3. Run the code in ASGCN, PWCN and RGAT.

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.