@inproceedings{song-2025-system,
    title = "System Report for {CCL}25-Eval Task 12: Surpassing {LLM}s with a Simple Pipeline for {M}andarin Spoken Entity-Relation Extraction",
    author = "Song, Wuganjing",
    editor = "Lin, Hongfei  and
      Li, Bin  and
      Tan, Hongye",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-2.56/",
    pages = "466--469",
    abstract = "``We present a strong and practical pipeline system for Mandarin spoken entity and relation extraction (Spoken-ERE), which integrates an industrial-grade ASR module (FireRedASR) with a span-based joint entity-relation extraction model. Unlike recent approaches that rely on large language models (LLMs) for end-to-end spoken information extraction, our method uses a modular pipeline design that is lightweight, interpretable, and easy to deploy. Despite its simplicity,our system achieves top-tier performance in a recent shared task workshop, outperform-ing several 5{\texttimes} larger LLM-based systems for 20{\%} on F1-score. We demonstrate through experiments that with robust ASR and a well-designed span-based model, classical pipelines re-main competitive and, in some scenarios, even preferable to LLM-based solutions for spoken information extraction in Mandarin.''"
}