Exploring Energy-Based Models for Out-of-Distribution Detection in Dialect Identification

TL;DR

This paper proposes a novel energy-based model with margin enhancement for improved out-of-distribution dialect detection, demonstrating superior performance over traditional softmax-based methods through extensive experiments.

Contribution

Introduction of MEJEM, a joint energy model with margin enhancement, for robust OOD dialect detection, integrating energy scores and sharpness-aware training.

Findings

Energy score outperforms softmax score in OOD detection

Sharpness-Aware Minimization improves model generalization

Experiments validate effectiveness of energy-based models in dialect identification

Abstract

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.