Situated Learning and Understanding of Natural Language
Speaker: Yoav Artzi, University of Washington
Location: Warren Weaver Hall 1302
Date: March 9, 2015, 11:30 a.m.
Host: Subhash Khot
Robust language understanding systems have the potential to transform how we interact with computers. However, significant challenges in automated reasoning and learning remain to be solved before we achieve this goal. To accurately interpret user utterances, for example when instructing a robot, a system must jointly reason about word meaning, grammatical structure, conversation history and world state. Additionally, to learn without prohibitive data annotation costs, systems must automatically make use of weak, situated linguistic cues for autonomous language learning.
In this talk, I will present a framework that uses situated interactions to learn to map sentences to rich, logical meaning representations. The approach jointly induces the structure of a complex natural language grammar and estimates its parameters, while relying on various learning cues, such as easily gathered demonstrations and even raw conversations without any additional annotation effort. It achieves state-of-the-art performance on a number of tasks, including robotic interpretation of navigational directions and learning to understand user utterances in dialog systems. Such an approach, when integrated into complete systems, has the potential to achieve continuous, autonomous learning by participating in actual interactions with users.
Yoav Artzi is a Ph.D. candidate in the Computer Science & Engineering department at the University of Washington, Seattle. His research interests are in the intersection of natural language processing and machine learning. In particular, he focuses on designing latent variable learning algorithms that recover rich representations of linguistic meaning for situated natural language understanding. He completed a B.Sc. summa cum laude in Computer Science in Tel Aviv University, and is a recipient of the 2014 Microsoft Research PhD Fellowship and the 2012 Yahoo Key Scientific Challenge award.
In-person attendance only available to those with active NYU ID cards.