Erik Altman, Jovan Blanusa, et al.
NeurIPS 2023
In machine-learning-based natural language processing, methods with high accuracy have been proposed for stance detection tasks. However, when they are applied to specific domains, they are often inaccurate due to domain-specific expressions. We propose an automated metamorphic testing method using transitive relations for creating training data that specializes stance detection in a specific domain. By specializing IBM Debater's stance detection in currency exchange domain, we confirmed our proposed method can improve the accuracy of judging the currency exchange-related sentences.
Erik Altman, Jovan Blanusa, et al.
NeurIPS 2023
Conrad Albrecht, Jannik Schneider, et al.
CVPR 2025
Haoran Zhu, Pavankumar Murali, et al.
NeurIPS 2020
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025