[Theory] REMINDER: Talks at TTIC: Yoon Kim, Harvard University

[Mary Marre]

*When:*      Wednesday, January 15th at 11:00 am



*Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526



*Who: *       Yoon Kim, Harvard University


*Title*:        Deep Learning and Language Structure

*Abstract*: Natural language has inherent structure. Words compose with one
another to form hierarchical structures to convey meaning. These
compositional structures are ubiquitous in all levels of language. However,
despite the recent, enormous success of deep neural networks in NLP,
capturing such discrete, combinatorial structure remains challenging. In
this talk, I will present two directions towards an integration of deep
learning and language structure. First, we will see how language structure
can be used as a rich source of prior knowledge to improve language
modeling and representation learning. Second, we will explore how advances
in model parameterization and inference, in particular deep learning, can
be used as a computational tool to discover linguistic structure from raw
text.


*Host:* Kevin Gimpel <kgimpel at ttic.edu>



Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 517*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Thu, Jan 9, 2020 at 10:30 AM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*      Wednesday, January 15th at 11:00 am
>
>
>
> *Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>
>
>
> *Who: *       Yoon Kim, Harvard University
>
>
> *Title*:        Deep Learning and Language Structure
>
> *Abstract*: Natural language has inherent structure. Words compose with
> one another to form hierarchical structures to convey meaning. These
> compositional structures are ubiquitous in all levels of language. However,
> despite the recent, enormous success of deep neural networks in NLP,
> capturing such discrete, combinatorial structure remains challenging. In
> this talk, I will present two directions towards an integration of deep
> learning and language structure. First, we will see how language structure
> can be used as a rich source of prior knowledge to improve language
> modeling and representation learning. Second, we will explore how advances
> in model parameterization and inference, in particular deep learning, can
> be used as a computational tool to discover linguistic structure from raw
> text.
>
>
> *Host:* Kevin Gimpel <kgimpel at ttic.edu>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 517*
> *Chicago, IL  60637*
> *p:(773) 834-1757*
> *f: (773) 357-6970*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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