End-to-End Emotion-Cause Pair Extraction based on SlidingWindow Multi-Label Learning

This repository contains the code for our EMNLP 2020 paper:

Zixiang Ding, Rui Xia, Jianfei Yu. End-to-End Emotion-Cause Pair Extraction based on SlidingWindow Multi-Label Learning. EMNLP 2020.

Please cite our paper if you use this code.

Dependencies

• Python 2 (tested on python 2.7.15)
• Tensorflow 1.13.1
• BERT (The pretrained BERT model "BERT-Base, Chinese" is required. Our model is built based on this implementation: https://github.com/google-research/bert)

Usage

python main.py

Results

Experimental results under two different data split setttings:

• 10-fold cross-validation, following NUSTM/ECPE (already reported in our paper)

Approach	Emotion-Cause Pair Ext.			Emotion Ext.			Cause Ext.
	P	R	F1	P	R	F1	P	R	F1
ECPE-MLL(ISML)	0.7090	0.6441	0.6740	0.8582	0.8429	0.8500	0.7248	0.6702	0.6950
ECPE-MLL(BERT)	0.7700	0.7235	0.7452	0.8608	0.9191	0.8886	0.7382	0.7912	0.7630

• Randomly sampling train/validation/test sets with 8:1:1 proportion 20 times, following HLT-HITSZ/TransECPE

Approach	Emotion-Cause Pair Ext.			Emotion Ext.			Cause Ext.
	P	R	F1	P	R	F1	P	R	F1
ECPE-MLL(ISML)	0.6725	0.6248	0.6471	0.8415	0.8212	0.8310	0.6864	0.6443	0.6639
ECPE-MLL(BERT)	0.7488	0.6976	0.7220	0.8465	0.8990	0.8717	0.7051	0.7704	0.7358