@inproceedings{ronning-etal-2018-sluice,
    title = "Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees",
    author = "R{\o}nning, Ola  and
      Hardt, Daniel  and
      S{\o}gaard, Anders",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/N18-2038/",
    doi = "10.18653/v1/N18-2038",
    pages = "236--241",
    abstract = "Sluice resolution in English is the problem of finding antecedents of \textit{wh}-fronted ellipses. Previous work has relied on hand-crafted features over syntax trees that scale poorly to other languages and domains; in particular, to dialogue, which is one of the most interesting applications of sluice resolution. Syntactic information is arguably important for sluice resolution, but we show that multi-task learning with partial parsing as auxiliary tasks effectively closes the gap and buys us an additional 9{\%} error reduction over previous work. Since we are not directly relying on features from partial parsers, our system is more robust to domain shifts, giving a 26{\%} error reduction on embedded sluices in dialogue."
}