StructBias

This repository contains code and data for COLING 2022 paper titled Structural Bias For Aspect Sentiment Triplet Extraction.

**************************** Updates ****************************

• 1/9/2022: We released our paper, code, and data. Check it out!

Quick Links

• Overview
• Getting Started
  • Requirements
  • Training
  • Evaluation
• Case Study
• Bugs or Questions?
• Citation

Overview

We propose to address the parameter and latency inefficiencies of incorporating structural bias in language models by using an adapter inplace of the stacked modules and using a cheap-to-compute relative position structure in place of the syntactic dependency structure (MuG with structural adapter). We release a large-scale ASTE dataset for more confident evaluation for ASTE (Lasted).

Getting Started

Requirements

• PyTorch
• Numpy
• Transformers

Training

Data

The processed data is placed in datasets/lasted.

Training scripts

We provide example training scripts for MuG with and without the structural adapter. For example, in scripts/run_google_bert_train.sh, we provide an example for training MuG without the adapter. We explain the arguments in following:

• --mode: Train or evaluate the model, can be chosen from train and evaluate.
• --pretrained_model_name_or_path: Pre-trained checkpoints to start with, e.g., bert-base-uncased.
• --embed_learning_rate: Learning rate for BERT backbones and adapters, e.g., 5e-5.
• --learning_rate: Learning rate for modules built upon BERT backbones, e.g., 5e-4.
• --hidden_size: Size of hidden states, e.g., 768.
• --sentiment_size: Number of types of sentiments, e.g., 4 for POS, NEG, NEU, and NONE.
• --tag_size: Number of types of tags, e.g., 3 for B, I, and O.
• --use_adapter: Use the adapter or not.

Logs

For results in the paper, we use Nvidia V100 GPUs with CUDA 11. Using different types of devices or different versions of CUDA/other softwares may lead to slightly different performance. The experimental logs can be found in logs for sanity checks.

Evaluation

We also provide example training scripts, for example scripts/run_google_bert_eval.sh, where arguments share similar meaning as those in training ones.

Case Study

We present a few cases of recommedation reason extraction from comments, as shown in cases/rec.txt.

Bugs or Questions?

If you have any questions related to the code or the paper, feel free to email Chen (czhang@bit.edu.cn). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!

Citation

Please cite our paper if you use the code or data in your work:

@inproceedings{zhang2022struct,
   title={Structural Bias For Aspect Sentiment Triplet Extraction},
   author={Zhang, Chen and Ren, Lei and Ma, Fang and Wang, Jingang and Wu, Wei and Song, Dawei},
   booktitle={COLING},
   year={2022}
}