@inproceedings{hung-etal-2020-complete,
    title = "A Complete Shift-Reduce {C}hinese Discourse Parser with Robust Dynamic Oracle",
    author = "Hung, Shyh-Shiun  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.acl-main.13/",
    doi = "10.18653/v1/2020.acl-main.13",
    pages = "133--138",
    abstract = "This work proposes a standalone, complete Chinese discourse parser for practical applications. We approach Chinese discourse parsing from a variety of aspects and improve the shift-reduce parser not only by integrating the pre-trained text encoder, but also by employing novel training strategies. We revise the dynamic-oracle procedure for training the shift-reduce parser, and apply unsupervised data augmentation to enhance rhetorical relation recognition. Experimental results show that our Chinese discourse parser achieves the state-of-the-art performance."
}