Reminder: this is in 10!
On Tue, Jan 16, 2018, 12:56 Michael Noukhovitch mnoukhov@gmail.com wrote:
*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.