Computer Science
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).
Title: A family of neural models for voice query understanding on an entertainment platform
CLIP Computing Infrastructure News
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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