Events & News
CS Dissertation Defense
| Category | Lecture |
| Date | Tuesday, April 29, 2014 |
| Time | 3:30 pm |
| Concludes | 5:30 pm |
| Location | Gould-Simpson 701 |
| Details | Review Committee: Paul Cohen, Mihai Surdeanu, Kobus Barnard & Carole Beale |
| Speaker | Jeremy Wright |
| Title | Ph.D. Candidate |
| Affiliation | Computer Science, University of Arizona |
Acquiring the Syntax and Semantics of Spatial Referring Expressions
To be useful for communication language must be grounded in perceptions of the world. But acquiring such grounded language is a challenging task, as most language that a learning agent encounters will have significant syntactic and semantic complexity. Several state of the art methods exist to learn complex grounded language from unannotated utterances, however each requires that the semantic system of the language be completely defined ahead of time. This expectation is problematic as it assumes not only that agents must have complete semantic understanding before starting to learn language, but also that the human designers of these systems can accurately transcribe the semantics of human languages in great detail.
We present Reagent (Referring Expression Agent), a system for concurrently learning the syntax and semantics of complex English referring expressions, with an emphasis on spatial referring expressions. Rather than requiring fully predefined semantic representations, Reagent only requires access to a set of semantic primitives from which it can build appropriate representations. We present results demonstrating that Reagent can acquire constructions that are missing from its initial grammar by observing the contextual utterances of a fully fluent agent. Reagent can approach fluent accuracy at inferring the referent of such expressions, and learns meanings that are qualitatively similar to the constructions of the agent from which it is learning. We propose that this approach could be expanded to other types of expressions and other languages, and could form a foundation for general natural language acquisition.