Reading to Learn: Improving Generalization by Learning From Language

Speaker: Victor ZHONG
         University of Washington

Title:   "Reading to Learn: Improving Generalization by Learning From
         Language"

Date:    Tuesday; 17 January 2023

Time:    10:00am - 11:00am HKT

Zoom link:
https://hkust.zoom.us/j/465698645?pwd=aVRaNWs2RHNFcXpnWGlkR05wTTk3UT09

Meeting ID:      465 698 645
Passcode:        20222023

Abstract:

Traditional machine learning (ML) systems are trained on vast quantities
of annotated data or experience. These systems often do not generalize to
new, related problems that emerge after training, such as conversing about
new topics or interacting with new environments. In this talk, I present
Reading to Learn, a new class of algorithms that improve generalization by
learning to read language specifications, without requiring any actual
experience or labeled examples. This includes, for example, reading FAQ
documents to learn to answer new questions and reading manuals to learn to
play new games. I will discuss new algorithms and data for Reading to
Learn applied to a broad range of tasks, including pretraining for
grounded reinforcement learning, data synthesis for code generation, and
task-oriented dialogue about new topics, while also highlighting open
challenges for this line of work.  Ultimately, the goal of Reading to
Learn is to democratize AI by making it accessible for low-resource
problems where the practitioner cannot obtain annotated data at scale, but
can instead write language specifications that models read to generalize.


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Biography:

Victor Zhong is a PhD student at the University of Washington Natural
Language Processing group. His research is at the intersection of natural
language processing and machine learning, with an emphasis on how to use
language understanding to learn more generally and more efficiently. His
research covers a range of topics, including dialogue, code generation,
question answering, and grounded reinforcement learning. Victor has been
awarded the Apple AI/ML Fellowship as well as an EMNLP Outstanding Paper
award. His work has been featured in Wired, MIT Technology Review,
TechCrunch, VentureBeat, Fast Company, and Quanta Magazine. He was a
founding member of Salesforce Research, and has previously worked at Meta
AI Research and Google Brain. He obtained a Masters in Computer Science
from Stanford University and a Bachelor of Applied Science in Computer
Engineering from the University of Toronto.