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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).

TitleA family of neural models for voice query understanding on an entertainment platform


Title: Event Semantics in Text Constructions, Vision, and Human-Robot Dialogue

Title: SCRIPTS: a System for Cross Language Information Processing,Translation and Summarization

Abstract: This presentation will give an overview of the research conducted by CLIP students and facuty to develop a System for Cross Language Information Processing, Translation and Summarization, as part of the IARPA MATERIAL program.

CLIP lab members get 5 minutes each to talk about their summer research. All are welcome to attend.

 

Language Science Center faculty -  Philip Resnik (Linguistics, UMIACS), Marine Carpuat (Computer Science, UMIACS), and Hal Daume (Computer Science, UMIACS) - join Douglas Oard (iSchool) on a four-year $14.4M Intelligence Advanced Research Projects Activity (IARPA) grant.

Title: Policy and Regulatory Semantics for Compliance Assistance

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