Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees
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.
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