@inproceedings{wang-etal-2025-mining,
    title = "Mining Word Boundaries from Speech-Text Parallel Data for Cross-domain {C}hinese Word Segmentation",
    author = "Wang, Xuebin  and
      Zhang, Lei  and
      Li, Zhenghua  and
      Zhou, Shilin  and
      Gong, Chen  and
      Hou, Yang",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.83/",
    pages = "1247--1257",
    abstract = "Inspired by early research on exploring naturally annotated data for Chinese Word Segmentation (CWS), and also by recent research on integration of speech and text processing, this work for the first time proposes to explicitly mine word boundaries from parallel speech-text data. We employ the Montreal Forced Aligner (MFA) toolkit to perform character-level alignment on speech-text data, giving pauses as candidate word boundaries. Based on detailed analysis of collected pauses, we propose an effective probability-based strategy for filtering unreliable word boundaries. To more effectively utilize word boundaries as extra training data, we also propose a robust complete-then-train (CTT) strategy. We conduct cross-domain CWS experiments on two target domains, i.e., ZX and AISHELL2. We have annotated about 1K sentences as the evaluation data of AISHELL2. Experiments demonstrate the effectiveness of our proposed approach."
}