DMSC_FEDA

Document Level Multi-Aspect Rating Prediction

This repository is a pytorch implementation for the following arxiv paper:

Deliberate Self-Attention Network with Uncertainty Estimation for Multi-Aspect Review Rating Prediction

Tian Shi, Ping Wang, Chandan K. Reddy

Requirements

• Python 3.6.9
• argparse=1.1
• torch=1.4.0
• sklearn=0.22.2.post1
• numpy=1.18.2

Dataset

Some data sources:

• TripAdvisor-R: https://github.com/HKUST-KnowComp/DMSC
• TripAdvisor-RU: https://github.com/Junjieli0704/HUARN
• TripAdvisor-B: https://github.com/HKUST-KnowComp/VWS-DMSC
• BeerAdvocate-R: https://github.com/HKUST-KnowComp/DMSC
• BeerAdvocate-B: https://github.com/HKUST-KnowComp/VWS-DMSC

Please download processed dataset from here. Place them along side with DMSC_FEDA.

|--- DMSC_FEDA
|--- Data
|    |--- trip_rate
|    |--- trip_binary
|    |    |--- dev
|    |    |--- dev.bert
|    |    |--- glove_42B_300d.npy
|    |    |--- test
|    |    |--- test.bert
|    |    |--- train
|    |    |--- train.bert
|    |    |--- vocab
|    |    |--- vocab_glove_42B_300d
|--- nats_results (results, automatically build)

Usuage

Training, Validate, Testing python3 run.py --task train Testing only python3 run.py --task test Evaluation python3 run.py --task evaluate keywords Extraction python3 run.py --task keywords Attention Weight Visualization python3 run.py --task visualization

If you want to run baselines, you may need un-comment the corresponding line in run.py.

Baselines Implemented.

Model	BRIEF
mtCNN	CNN + Multi-Task Learning
mtRNN	Bi-LSTM + Multi-Task Learning
mtAttn	mtRNN + Self-Attention
mtBertAttn	BERT + Multi-Task Learning + Self-Attention
mtAttnDA	mtRNN + Deliberated Self-Attention
MtAttnFE	mtAttn + Pretrained Embedding + Feature Enrichment
FEDA	mtAttnDA + Pretrained Embedding + Feature Enrichment

Use Pretrained Model

Coming Soon.

Citation

@article{shi2020deliberate,
  title={Deliberate Self-Attention Network with Uncertainty Estimation for Multi-Aspect Review Rating Prediction},
  author={Shi, Tian and Wang, Ping and Reddy, Chandan K},
  journal={arXiv preprint arXiv:2009.09112},
  year={2020}
}