Modular Neural Architectures for Grounded Language Learning
, UC Berkeley
Date: Wednesday, February 01, 2017
Time: 2:00 PM to 3:00 PM Note: all times are in the Eastern Time Zone
Location: 32-D463 (Stata Center - Star Conference Room)
Host: Regina Barzilay, MIT CSAIL
Contact: Marcia G. Davidson, 617-253-3049, email@example.com
Speaker URL: None
TALK: Modular Neural Architectures for Grounded Language Learning
Language understanding depends on two abilities: an ability to translate between natural language utterances and representations of meaning, and an ability to relate these meaning representations to the world. In the natural language processing literature, these tasks are respectively known as "semantic parsing" and "grounding", and have been treated as essentially independent problems. In this talk, I will present a family of neural architectures for jointly learning to ground language in the world and reason about it compositionally.
I will begin by describing a model that uses syntactic information to dynamically construct neural networks from composable primitives. The resulting composed networks can be used to achieve state-of-the-art results on a variety of grounded question answering tasks. Next, I will present a model for contextual referring expression generation, in which contrastive behavior results from a combination of learned semantics and inference-driven pragmatics. This model is again backed by modular neural components---in this case elementary listener and speaker representations. If time permits, I will conclude by discussing recent work on using language-like "sketches" to learn modular policy representations for interactive environments.
Created by Marcia G. Davidson at Tuesday, January 24, 2017 at 5:00 PM.