[Colloquium] Fwd: TODAY (Now REMOTE ONLY): Data Science/CS Candidate Talk - Alane Suhr (Cornell)

[Rob Mitchum]

Reminder: this talk has been moved to remote-only. Please attend via Zoom
or Live Stream links below, or see:

https://datascience.uchicago.edu/events/alane-suhr-cornell-reasoning-and-learning-in-interactive-natural-language-systems/

On Mon, Mar 28, 2022 at 8:54 AM Rob Mitchum <rdmitchum at gmail.com> wrote:

> *Data Science Institute/Computer Science Candidate Seminar*
>
> *Alane Suhr*
> *PhD Candidate*
> *Cornell University*
>
> *Monday, March 28th*
> *3:00 p.m. - 4:00 p.m.*
> *REMOTE ONLY: Live Stream <http://live.cs.uchicago.edu/alanesuhr/> or Zoom
> <https://uchicago.zoom.us/j/96377760382?pwd=QnRKa1d0ZkNBamw3SGJ3Q0lBRThzdz09> (details
> below)*
>
>
> *Reasoning and Learning in Interactive Natural Language Systems*
>
> Systems that support expressive, situated natural language interactions
> are essential for expanding access to complex computing systems, such as
> robots and databases, to non-experts. Reasoning and learning in such
> natural language interactions is a challenging open problem. For example,
> resolving sentence meaning requires reasoning not only about word meaning,
> but also about the interaction context, including the history of the
> interaction and the situated environment. In addition, the sequential
> dynamics that arise between user and system in and across interactions make
> learning from static data, i.e., supervised data, both challenging and
> ineffective. However, these same interaction dynamics result in ample
> opportunities for learning from implicit and explicit feedback that arises
> naturally in the interaction. This lays the foundation for systems that
> continually learn, improve, and adapt their language use through
> interaction, without additional annotation effort. In this talk, I will
> focus on these challenges and opportunities. First, I will describe our
> work on modeling dependencies between language meaning and interaction
> context when mapping natural language in interaction to executable code. In
> the second part of the talk, I will describe our work on language
> understanding and generation in collaborative interactions, focusing on
> continual learning from explicit and implicit user feedback.
>
> *Bio*: Alane Suhr <https://www.alanesuhr.com/> is a PhD Candidate in the
> Department of Computer Science at Cornell University, advised by Yoav
> Artzi. Her research spans natural language processing, machine learning,
> and computer vision, with a focus on building systems that participate and
> continually learn in situated natural language interactions with human
> users. Alane’s work has been recognized by paper awards at ACL and NAACL,
> and has been supported by fellowships and grants, including an NSF Graduate
> Research Fellowship, a Facebook PhD Fellowship, and research awards from
> AI2, ParlAI, and AWS. Alane has also co-organized multiple workshops and
> tutorials appearing at NeurIPS, EMNLP, NAACL, and ACL. Previously, Alane
> received a BS in Computer Science and Engineering as an Eminence Fellow at
> the Ohio State University.
>
> *Host*: Chenhao Tan
>
> *Zoom Info:*
> https://uchicago.zoom.us/j/96377760382?pwd=QnRKa1d0ZkNBamw3SGJ3Q0lBRThzdz09
> Meeting ID: 963 7776 0382
> Password: ds2022
>


-- 
*Rob Mitchum*

*Associate Director of Communications for Data Science and Computing*
*University of Chicago*
*rmitchum at uchicago.edu <rmitchum at ci.uchicago.edu>*
*773-484-9890*
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