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PRODID:-//Cornell U. Department of Computer Science//Brown Bag Seminar//EN
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SUMMARY:Brown bag: Yoav Artzi
DESCRIPTION:Title: Situated Learning and Understanding of Natural
	 Language\nSpeaker: Yoav Artzi\nAbstract: 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
	 interaction cues for autonomous language learning.\nIn 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.
LOCATION:Gates 122
UID:2015-09-29
STATUS:CONFIRMED
DTSTART:20150929T160000Z
DTEND:20150929T170000Z
LAST-MODIFIED:20150827T155212Z
ORGANIZER;CN=Jonathan Shi:http://www.cs.cornell.edu/~jshi/brownbag/
DTSTAMP:20260408T121819Z
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