Explanation-Based Bias Decoupling Regularization for Improving Natural Language Inference Models

Explanation-based Bias Decoupling Regularization (EBD-Reg) trains natural language inference models to distinguish and decouple task-relevant keywords from biases, enabling them to focus on the intended features and improve out-of-distribution inference performance.