[Lisa_seminaires] [Tea Talk] Timothy O'Donnell (McGill) Fri Jan 19 10:30AM AA1360

Michael Noukhovitch mnoukhov at gmail.com
Mar 16 Jan 12:56:55 EST 2018


*Note: *For the new year, we're moving to a different room, *AA1360* on the
first floor! Exciting!

For the first tea talk of the year we are going to have *Timothy O'Donnell*,
professor at *McGill,* giving a talk on *Friday Jan 19th* at* 10:30AM* in
room *AA1360*.

This should be a real gronk of talk, and I'd encourage everyone to attend
to it!
Michael

*KEYWORDS *Computational Linguistics, Language Learning, Linguistic
Structure

*TITLE* Computation, Storage, and Generalization in Language

*ABSTRACT*
A celebrated property of natural language is creativity, the ability
to combine stored units to derive new expressions. This feature is
found across multiple levels of linguistic structure: Sounds can be
combined to form novel roots, suffixes, and affixes, which can be
combined to form novel words, which can be combined to form novel
sentences. However, each human language is characterized by its own
inventory of units at each level and its own constraints on their
combination. A syllable like "derp" sounds like a better morpheme of
English than a syllable like "denp." The existence of the words "warmth"
and "truth" do not imply the possibility of "coolth," but
"warmness," "trueness," and "coolness" are all grammatical. And speakers of
English will naturally drop the phrase "on the counter" from "John made
dinner on the counter", but not from "John put dinner on the counter".

How do learners acquire the inventory of units and constraints at each
level of linguistic structure that are particular to their own language? I
will discuss a theoretical framework designed to address this question and
a number of specific applications. The approach is based on the idea that
this problem can be approached by formulating a variety of
linguistic learning problems as problems of program induction and then
optimizing a tradeoff between a pressure to store fewer, more reusable
primitive units and a pressure to account for each linguistic expression in
as few computational steps as possible. Although the idea behind this
tradeoff is an old one, it has surprisingly deep and
far-reaching consequences when applied to domain-specific models of
linguistic computation. I will show how this approach can shed light on a
number of problems across the linguistic system.


*BIO*
Tim O'Donnell is an assistant professor in the department of linguistics at
McGill University. Previously he was a research scientist at MIT in the
department of Brain and Cognitive Sciences.
His research focuses on developing mathematical and computational models of
language learning and processing. His work draws on techniques from
computational linguistics, and artificial intelligence integrating ideas
from theoretical linguistics and methods from experimental psychology.
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