[Theory] NOW: 3/2 Talks at TTIC: Alane Suhr, Cornell University
Mary Marre
mmarre at ttic.edu
Wed Mar 2 11:29:39 CST 2022
*When:* Wednesday, March 2nd at* 11:30 am CT*
*Where: *Talk will be given *live, in-person* at
TTIC, 6045 S. Kenwood Avenue
5th Floor, Room 530
*Where:* Zoom Virtual Talk (*register in advance here
<https://uchicagogroup.zoom.us/webinar/register/WN_bA-GXT9mRnu7oUJXbbBKXA>*)
*Who: * Alane Suhr, Cornell University
*Title: *Reasoning and Learning in Interactive Natural Language
Systems
*Abstract: *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 environments, focusing on
learning to recover from errors and on continual learning from explicit and
implicit user feedback.
*Bio: *Alane Suhr 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:* Karen Livescu <klivescu at ttic.edu>*
Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Chicago, IL 60637*
*mmarre at ttic.edu <mmarre at ttic.edu>*
On Wed, Mar 2, 2022 at 10:32 AM Mary Marre <mmarre at ttic.edu> wrote:
> *When:* Wednesday, March 2nd at* 11:30 am CT*
>
>
> *Where: *Talk will be given *live, in-person* at
>
> TTIC, 6045 S. Kenwood Avenue
>
> 5th Floor, Room 530
>
>
> *Where:* Zoom Virtual Talk (*register in advance here
> <https://uchicagogroup.zoom.us/webinar/register/WN_bA-GXT9mRnu7oUJXbbBKXA>*
> )
>
>
> *Who: * Alane Suhr, Cornell University
>
>
>
>
> *Title: *Reasoning and Learning in Interactive Natural Language
> Systems
>
> *Abstract: *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 environments, focusing on
> learning to recover from errors and on continual learning from explicit and
> implicit user feedback.
>
> *Bio: *Alane Suhr 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:* Karen Livescu <klivescu at ttic.edu>*
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Chicago, IL 60637*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
>
> On Tue, Mar 1, 2022 at 1:27 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:* Wednesday, March 2nd at* 11:30 am CT*
>>
>>
>> *Where: *Talk will be given *live, in-person* at
>>
>> TTIC, 6045 S. Kenwood Avenue
>>
>> 5th Floor, Room 530
>>
>>
>> *Where:* Zoom Virtual Talk (*register in advance here
>> <https://uchicagogroup.zoom.us/webinar/register/WN_bA-GXT9mRnu7oUJXbbBKXA>*
>> )
>>
>>
>> *Who: * Alane Suhr, Cornell University
>>
>>
>>
>>
>> *Title: *Reasoning and Learning in Interactive Natural Language
>> Systems
>>
>> *Abstract: *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 environments, focusing on
>> learning to recover from errors and on continual learning from explicit and
>> implicit user feedback.
>>
>> *Bio: *Alane Suhr 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:* Karen Livescu <klivescu at ttic.edu>*
>>
>>
>> Mary C. Marre
>> Faculty Administrative Support
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Chicago, IL 60637*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>>
>> On Wed, Feb 23, 2022 at 6:13 PM Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *When:* Wednesday, March 2nd at* 11:30 am CT*
>>>
>>>
>>> *Where: *Talk will be given *live, in-person* at
>>>
>>> TTIC, 6045 S. Kenwood Avenue
>>>
>>> 5th Floor, Room 530
>>>
>>>
>>> *Where:* Zoom Virtual Talk (*register in advance here
>>> <https://uchicagogroup.zoom.us/webinar/register/WN_bA-GXT9mRnu7oUJXbbBKXA>*
>>> )
>>>
>>>
>>> *Who: * Alane Suhr, Cornell University
>>>
>>>
>>>
>>>
>>> *Title: *Reasoning and Learning in Interactive Natural Language
>>> Systems
>>>
>>> *Abstract: *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 environments, focusing on
>>> learning to recover from errors and on continual learning from explicit and
>>> implicit user feedback.
>>>
>>> *Bio: *Alane Suhr 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:* Karen Livescu <klivescu at ttic.edu>*
>>>
>>>
>>>
>>>
>>>
>>> Mary C. Marre
>>> Faculty Administrative Support
>>> *Toyota Technological Institute*
>>> *6045 S. Kenwood Avenue*
>>> *Chicago, IL 60637*
>>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>>
>>
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