  The diverse nature of dialects presents challenges for models trained on
specific linguistic patterns, rendering them susceptible to errors when
confronted with unseen or out-of-distribution (OOD) data. This study introduces
a novel margin-enhanced joint energy model (MEJEM) tailored specifically for
OOD detection in dialects. By integrating a generative model and the energy
margin loss, our approach aims to enhance the robustness of dialect
identification systems. Furthermore, we explore two OOD scores for OOD dialect
detection, and our findings conclusively demonstrate that the energy score
outperforms the softmax score. Leveraging Sharpness-Aware Minimization to
optimize the training process of the joint model, we enhance model
generalization by minimizing both loss and sharpness. Experiments conducted on
dialect identification tasks validate the efficacy of Energy-Based Models and
provide valuable insights into their performance.
