Introduction

This repository is used in our paper:

JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection
Bin Liang, Qinlin Zhu, Xiang Li, Min Yang, Lin Gui, Yulan He, Ruifeng Xu^*. Proceedings of ACL 2022

Please cite our paper and kindly give a star for this repository if you use this code.

Requirements

• Python 3.6
• PyTorch 1.6.0
• faiss-gpu 1.7.1
• transformers 2.5.1

Usage

• Install faiss package.

Training

• Train with command, optional arguments could be found in run_semeval.py & run_vast.py & run_wtwt.py

• Run Semeval dataset: python ./run_semeval.py

• Run VAST dataset: python ./run_vast.py

• Run WTWT dataset: python ./run_wtwt.py

Reproduce & Peformance

• Due to the small number of dataset samples (especially SEM16), the performance gap between different seeds will vary greatly, please tune the parameter of --seed for better performance.
• We have provided checkpoints that are superior or equal to the performance reported in the paper.
• Please Run python files in run_checkpoints, you can use the trained model for prediction, and the model can be downloaded from Google drives.
• We also use 5 random seeds to run the code directly without any other tuning parameters. The performance is as follows:

Task	Dataset	Target	Reported	Checkpoint	seed1	seed2	seed3	seed4	seed5	Mean	Max	Gap
Zero-shot	VAST	-	72.3	72.4	70.6	71.3	72.4	72.0	71.3	71.5	72.4	+0.1
	SEM16	DT	50.5	50.9	46.0	40.6	45.6	48.4	50.2	46.2	50.2	-0.3
		HC	54.8	56.4	50.7	56.4	45.7	55.9	51.3	52.0	56.4	+1.6
		FM	53.8	54.2	50.9	51.1	49.1	49.8	49.4	50.1	51.1	-2.7
		LA	49.5	55.5	54.8	54.3	55.5	51.3	47.1	52.6	55.5	+6
		A	54.5	54.6	55.1	48.0	60.0	55.4	55.2	54.7	60.0	+5.5
		CC	39.7	40.7	31.9	36.9	39.7	40.2	28.3	35.4	40.2	+0.5
	WTWT	CA	72.4	73.6	72.5	71.4	73.3	73.4	74.9	73.1	74.9	+2.5
		CE	70.2	70.9	70.1	71.4	70.4	70.3	70.3	70.1	71.4	+1.2
		AC	76.0	76.5	75.0	74.3	77.3	73.3	75.6	75.1	77.3	+1.3
		AH	75.2	76.5	76.2	76.1	76.0	77.9	78.0	76.8	78.0	+2.8
Few-shot	VAST	-	71.5	71.6	71.6	71.9	68.4	66.1	69.5	69.5	71.9	+0.4
Cross-target	SEM16	HC->DT	52.8	54.6	42.9	46.9	48.1	53.7	54.2	49.2	54.2	+1.4
		DT->HC	54.3	55.4	52.1	55.8	54.6	47.8	38.6	49.8	55.8	+1.5
		FM->LA	58.8	60.0	49.8	58.0	58.3	46.7	45.7	51.7	60.0	-0.5
		LA->FM	54.5	54.8	45.8	41.8	54.1	36.2	47.9	45.2	54.1	-0.4

Credits

• The code of this repository partly relies on ASGCN & ABSA-PyTorch & PCL.
• Here, I would like to express my gratitude to the authors of the ASGCN & ABSA-PyTorch & PCL repositories.