@inproceedings{im-etal-2025-deragec,
    title = "{D}e{RAGEC}: Denoising Named Entity Candidates with Synthetic Rationale for {ASR} Error Correction",
    author = "Im, Solee  and
      Lee, Wonjun  and
      An, JinMyeong  and
      Kim, Yunsu  and
      Ok, Jungseul  and
      Lee, Gary",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.findings-acl.786/",
    doi = "10.18653/v1/2025.findings-acl.786",
    pages = "15181--15193",
    ISBN = "979-8-89176-256-5",
    abstract = "We present DeRAGEC, a method for improving Named Entity (NE) correction in Automatic Speech Recognition (ASR) systems. By extending the Retrieval-Augmented Generative Error Correction (RAGEC) framework, DeRAGEC employs synthetic denoising rationales to filter out noisy NE candidates before correction. By leveraging phonetic similarity and augmented definitions, it refines noisy retrieved NEs using in-context learning, requiring no additional training. Experimental results on CommonVoice and STOP datasets show significant improvements in Word Error Rate (WER) and NE hit ratio, outperforming baseline ASR and RAGEC methods. Specifically, we achieved a 28{\%} relative reduction in WER compared to ASR without postprocessing."
}