@inproceedings{wang-etal-2020-combining,
    title = "Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection",
    author = "Wang, Shaolei  and
      Wang, Zhongyuan  and
      Che, Wanxiang  and
      Liu, Ting",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.142",
    doi = "10.18653/v1/2020.emnlp-main.142",
    pages = "1813--1822",
    abstract = "Most existing approaches to disfluency detection heavily rely on human-annotated corpora, which is expensive to obtain in practice. There have been several proposals to alleviate this issue with, for instance, self-supervised learning techniques, but they still require human-annotated corpora. In this work, we explore the unsupervised learning paradigm which can potentially work with unlabeled text corpora that are cheaper and easier to obtain. Our model builds upon the recent work on Noisy Student Training, a semi-supervised learning approach that extends the idea of self-training. Experimental results on the commonly used English Switchboard test set show that our approach achieves competitive performance compared to the previous state-of-the-art supervised systems using contextualized word embeddings (e.g. BERT and ELECTRA).",
}