Keyword-Aware ASR Error Augmentation for Robust Dialogue State Tracking
<|reference_start|>Keyword-Aware ASR Error Augmentation for Robust Dialogue State Tracking: Dialogue State Tracking (DST) is a key part of task-oriented dialogue systems, identifying important information in conversations. However, its accuracy drops significantly in spoken dialogue environments due to named entity errors from Automatic Speech Recognition (ASR) systems. We introduce a simple yet effective data augmentation method that targets those entities to improve the robustness of DST model. Our novel method can control the placement of errors using keyword-highlighted prompts while introducing phonetically similar errors. As a result, our method generated sufficient error patterns on keywords, leading to improved accuracy in noised and low-accuracy ASR environments.<|reference_end|>
@article{lee2024keyword-aware,
    title={Keyword-Aware ASR Error Augmentation for Robust Dialogue State Tracking},
    author={Jihyun Lee, Solee Im, Wonjun Lee, Gary Geunbae Lee},
    journal={arXiv preprint arXiv:2409.06263},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06263},
    primaryClass={cs.CL cs.AI}
}