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BERT-Aspect

Code for the paper "Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference" https://arxiv.org/pdf/2002.04815.pdf.

Requirements

python>=3.6
transformers==2.9.0
pytorch==1.5.0

Running the code

git clone https://github.com/avinashsai/BERT-Aspect.git
cd PyTorch

python main.py --dataset (laptop/ restaurant)
               --maxlen (Maximum Sentence length (default: 80))
               --numclasses (3 if "conflict" class is not included else 4 (default:3))
               --data-path (path to datasets (default: '../Data/))
               --batch-size (Batch Size (default: 8)
               --numepochs (Number of training epochs (default: 10))
               --runs (Number of average runs to report results (default: 10))
               --model_name (lstm /attention /base)

Note

This code is un-official implementation of the paper. Hence, training details may not be exactly similar. Also, I have made couple of changes due to which results are superior than the reported paper results.

Results

For Laptop dataset:

Model This Implementation Result (Acc) Paper Result (Acc)
BERT Base Uncased + Linear 75.44 74.66
BERT Base Uncased + LSTM 76 75.31
BERT Base Uncased + Attention 75.91 75.16
Model This Implementation Result (F1) Paper Result (F1)
BERT Base Uncased + Linear 70 68.64
BERT Base Uncased + LSTM 70.6 69.37
BERT Base Uncased + Attention 70.6 68.76

For Restaurant dataset:

Model This Implementation Result (Acc) Paper Result (Acc)
BERT Base Uncased + Linear 82.91 81.92
BERT Base Uncased + LSTM 83.04 82.21
BERT Base Uncased + Attention 83.29 82.38
Model This Implementation Result (F1) Paper Result (F1)
BERT Base Uncased + Linear 73.2 71.97
BERT Base Uncased + LSTM 73.4 72.52
BERT Base Uncased + Attention 73.6 73.22

For Twitter dataset:

Model This Implementation Result (Acc) Paper Result (Acc)
BERT Base Uncased + Linear 70.32 72.46
BERT Base Uncased + LSTM 70.66 73.06
BERT Base Uncased + Attention 69.06 73.35
Model This Implementation Result (F1) Paper Result (F1)
BERT Base Uncased + Linear 68.5 71.04
BERT Base Uncased + LSTM 67.1 71.61
BERT Base Uncased + Attention 69 71.88