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CLIP Colloquium: Daniel Gildea (Rochester)

Time: 
Wednesday, October 17, 2018 - 11:00 AM to 12:00 PM
Location: 
4172 A.V. Williams Building

Cache Transition Systems for Semantic Parsing

Abstract: We describe a transition system that generalizes standard transition-based dependency parsing techniques to generate a graph rather than a tree.  Our system includes a cache with fixed size m, and we characterize the relationship between the parameter m and the class of graphs that can be produced through the graph-theoretic concept of tree decomposition.  We train a sequence-to-sequence neural model based on this system to parse text into Abstract Meaning Representation (AMR).

Bio: Daniel Gildea is a professor of computer science at the University of Rochester, focusing on machine tanslation, semantic parsing, and text generation.