3 papers
cs.CL2025
Beyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language Models
Sangmin Song, Juhwan Choi, JungMin Yun +1
Large language models (LLMs) have demonstrated remarkable performance in zero-shot dialogue state tracking (DST), reducing the need for task-specific training. However, conventiona…
cs.CL2024
Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation
Kyohoon Jin, Junho Lee, Juhwan Choi +2
Efforts to leverage deep learning models in low-resource regimes have led to numerous augmentation studies. However, the direct application of methods such as mixup and cutout to t…
cs.CL2024
AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes
Juhwan Choi, Kyohoon Jin, Junho Lee +2
Text data augmentation is a complex problem due to the discrete nature of sentences. Although rule-based augmentation methods are widely adopted in real-world applications because…