@inproceedings{chen-etal-2018-pre,
    title = "Pre- and In-Parsing Models for Neural Empty Category Detection",
    author = "Chen, Yufei  and
      Zhao, Yuanyuan  and
      Sun, Weiwei  and
      Wan, Xiaojun",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P18-1250/",
    doi = "10.18653/v1/P18-1250",
    pages = "2687--2696",
    abstract = "Motivated by the positive impact of empty category on syntactic parsing, we study neural models for pre- and in-parsing detection of empty category, which has not previously been investigated. We find several non-obvious facts: (a) BiLSTM can capture non-local contextual information which is essential for detecting empty categories, (b) even with a BiLSTM, syntactic information is still able to enhance the detection, and (c) automatic detection of empty categories improves parsing quality for overt words. Our neural ECD models outperform the prior state-of-the-art by significant margins."
}