POS4ASC

POS4ASC - Exploiting Position Bias for Robust Aspect Sentiment Classification

• Our code and data for ACL 2021 paper titled "Exploiting Position Bias for Robust Aspect Sentiment Classification"
• Fang Ma, Chen Zhang, and Dawei Song.

Intuition

Robustness Issue

• State-of-the-art ASC models are recently shown to suffer from the issue of robustness. Particularly in two common scenarios: 1) out-of-domain (O.O.D.) scenario, and 2) adversarial (Adv.) scenario. A case is given below:

  

Position Bias Helps

• We hypothesize that position bias (i.e., the words closer to a concerning aspect would carry a higher degree of importance) is crucial for building more robust ASC models by reducing the probability of mis-attending. The hypothesis is statistically evidenced by existing benchmarks as below, aspect proximity depicts how close opinions are positioned around their corresponding aspects:

  

• From the observation, we propose two mechanisms for capturing position bias, namely position-biased weight and position-biased dropout, which can be flexibly injected into existing models to enhance word representations, thereby robustness of models.

Requirements

• python 3.8
• pytorch 1.7.0
• nltk
• transformer

Usage

• Download pretrained GloVe embeddings with this link and extract glove.840B.300d.txt into the root directory.
• Train a model with a command similar to those in run.sh or run_bert.sh, optional arguments could be found in run.py.
• An example of training with LSTM w/ position-biased weight

  python run.py 
      --model_name lstm 
      --train_data_name laptop 
      --mode train 
      --weight

• An example of training with Roberta w/ position-biased weight

  python run.py 
      --model_name roberta 
      --train_data_name laptop 
      --mode train 
      --learning_rate 1e-5 
      --weight_decay 0.0

• Evaluate the model with a command similar to those in run.sh or run_bert.sh, optional arguments could be found in run.py.
• An example of evaluation with LSTM w/ position-biased weight on the adversarial test set

  python run.py 
      --model_name lstm 
      --train_data_name laptop 
      --test_data_name arts_laptop 
      --mode evluate
      --weight

• An example of evaluation with Roberta w/ position-biased weight on the adversarial test set

  python run.py 
      --model_name roberta 
      --train_data_name laptop 
      --test_data_name arts_laptop 
      --mode evaluate 
      --weight

Citation

If you use the code in your paper, please kindly star this repo and cite our paper

@inproceedings{ma-etal-2021-exploiting, 
    title = "Exploiting Position Bias for Robust Aspect Sentiment Classification", 
    author = "Ma, Fang and Zhang, Chen and Song, Dawei", 
    booktitle = "Findings of ACL",
    year = "2021", 
    address = "Online", 
    publisher = "Association for Computational Linguistics",
} 

Issues

• For any issues or suggestions about this work, don't hesitate to create an issue or directly contact me via [email protected] !