CCChenhao997 / EMCGCN-ASTE

Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction

EMC-GCN

Code and datasets of our paper "Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction" accepted by ACL 2022.

Requirements

• python==3.7.6

• torch==1.4.0

• transformers==3.4.0

• argparse==1.1

Training

To train the EMC-GCN model, run:

cd EMC-GCN/code
sh run.sh

or

python main.py --mode train --dataset res14 --batch_size 16 --epochs 100 --model_dir savemodel/ --seed 1000 --pooling avg --prefix ../data/D2/

Inference

To test the performance of EMC-GCN, you only need to modify the --mode parameter.

python main.py --mode test --dataset res14 --batch_size 16 --epochs 100 --model_dir savemodel/ --seed 1000 --pooling avg --prefix ../data/D2/

Acknowledge

We appreciate all authors from this paper: "Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction", because the code in this repository is based on their work GTS.