Sluice resolution in English is the problem of finding antecedents of wh-fronted ellipses. Previous work has relied on handcrafted 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.
We present a technique for applying (forward and) reversemode automatic differentiation (AD) on a non-recursive secondorder functional array language that supports nested parallelism and is primarily aimed at efficient GPU execution.The key idea is to eliminate the need for a "tape" by relying on redundant execution to bring into each new scope all program variables that may be needed by the differentiated code. Efficient execution is enabled by the observation that perfectly-nested scopes do not introduce re-execution, and such perfect nests are produced by known compiler transformations, e.g., flattening. Our technique differentiates loops and bulk-parallel operators-such as map, reduce, histogram, scan, scatter-by specific rewrite rules, and aggressively optimizes the resulting nested-parallel code. We report an experimental evaluation that compares with established AD solutions and demonstrates competitive performance on nine common benchmarks from recent applied AD literature.
Sluicing resolution is the task of identifying the antecedent to a question ellipsis. Antecedents are often sentential constituents, and previous work has therefore relied on syntactic parsing, together with complex linguistic features. A recent model instead used partial parsing as an auxiliary task in sequential neural network architectures to inject syntactic information. We explore the linguistic information being brought to bear by such networks, both by defining subsets of the data exhibiting relevant linguistic characteristics, and by examining the internal representations of the network. Both perspectives provide evidence for substantial linguistic knowledge being deployed by the neural networks.
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