@inproceedings{chou-etal-2023-advancing,
    title = "Advancing Multi-Criteria {C}hinese Word Segmentation Through Criterion Classification and Denoising",
    author = "Chou, Tzu Hsuan  and
      Lin, Chun-Yi  and
      Kao, Hung-Yu",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2023.acl-long.356/",
    doi = "10.18653/v1/2023.acl-long.356",
    pages = "6460--6476",
    abstract = "Recent research on multi-criteria Chinese word segmentation (MCCWS) mainly focuses on building complex private structures, adding more handcrafted features, or introducing complex optimization processes. In this work, we show that through a simple yet elegant input-hint-based MCCWS model, we can achieve state-of-the-art (SoTA) performances on several datasets simultaneously. We further propose a novel criterion-denoising objective that hurts slightly on F1 score but achieves SoTA recall on out-of-vocabulary words. Our result establishes a simple yet strong baseline for future MCCWS research. Source code is available at \url{https://github.com/IKMLab/MCCWS}."
}