This repository was used in our paper:
“Multi-task Sequence Tagging for Emotion-Cause Pair Extraction via Tag Distribution Refinement”
Chuang Fan, Chaofa Yuan, Lin Gui, Yue Zhang, Ruifeng Xu. TASLP 2021
Please cite our paper if you use this code.
Python 3.7
Pytorch 1.1.0
CUDA 10.0
BERT - Our bert model is adapted from this implementation: https://github.com/huggingface/pytorch-pretrained-BERT
20-fold-data-splits - A dir where contains data splits following Fan et al.
train.pkl: A list where contains two items. train[0] is a list of document and train[1] is a list of the correspondding emotion-cause pairs. For example, train[0][0]="Last week, I lost my phone where shopping, I feel sad now", then train[1][0]=[(2, 1)].valid.pkl: Similar to train.pkl.test.pkl: Similar to train.pkl.
10-fold-data-splits - A dir where contains data splits following Xia and Ding. The data format is the same as 20-fold-data-splits.
bert-base-chinese - Put the download Pytorch bert model here.
Utils - A dir where contains several python scripts used in this code.
Evaluation.py: Used to evaluate the performance of the proposed model.Metrics.py: Metrics for emotion extraction, cause extraction and emotion-cause pair extractions.PrepareData.py: The scipt for preparing data.
Config.py - The script holds all the model configuration.
Modules.py - The script where contains the proposed multi-task sequence tagging model.
Run.py - The main script to train and evaluate the proposed model on different splits.
python3 Run.py