[Theory] NOW: 2/18 Talks at TTIC: Maarten Sap, University of Washington

[Mary Marre]

*When:*      Thursday, February 18th at* 11:10 am CT*



*Where:*     Zoom Virtual Talk (*register in advance here
<https://uchicagogroup.zoom.us/webinar/register/WN_FazOKn7bRGq8UdxYLJta_g>*)



*Who: *       Maarten Sap, University of Washington


*Title:        *Positive AI with Social Commonsense Models

*Abstract: *To effectively understand language and safely communicate with
humans, machines must not only grasp the surface meanings of texts, but
also their underlying social meaning. This requires understanding
interpersonal social commonsense, such as knowing to thank someone for
giving you a present, as well as understanding harmful social biases and
stereotypes. Failure to account for these social and power dynamics could
cause models to produce redundant, rude, or even harmful outputs.

In this talk, I will describe my research on enabling machines to reason
about social dynamics and social biases in text. I will first discuss
ATOMIC, the first large-scale knowledge graph of social and interpersonal
commonsense knowledge, with which machines can be taught to reason about
the causes and effects of everyday events. Then, I will show how we can
make machines understand and mitigate social biases in language, using
Social Bias Frames, a new structured formalism for distilling biased
implications of language, and PowerTransformer, a new unsupervised model
for controllable debiasing of text.

I will conclude with future research directions on making NLP systems more
socially-aware and equitable, and how to use language technologies for
positive societal impact.

*Bio: *Maarten Sap is a final year PhD student in the University of
Washington's natural language processing (NLP) group, advised by Noah Smith
and Yejin Choi. His research focuses on making NLP systems socially
intelligent, and understanding social inequality and bias in language. He
has presented his work in top-tier NLP and AI conferences, receiving a best
short paper nomination at ACL 2019 and a best paper award at the WeCNLP
2020 summit. Additionally, he and his team won the inaugural Amazon Alexa
Prize, a social chatbot competition. In the past, he has interned at the
Allen Institute for AI working on social commonsense reasoning, and at
Microsoft Research working on deep learning models for understanding human
cognition.

*Host:* Karen Livescu <klivescu at ttic.edu>



Mary C. Marre
Faculty Administrative Support
*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, Feb 18, 2021 at 10:00 AM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*      Thursday, February 18th at* 11:10 am CT*
>
>
>
> *Where:*     Zoom Virtual Talk (*register in advance here
> <https://uchicagogroup.zoom.us/webinar/register/WN_FazOKn7bRGq8UdxYLJta_g>*
> )
>
>
>
> *Who: *       Maarten Sap, University of Washington
>
>
> *Title:        *Positive AI with Social Commonsense Models
>
> *Abstract: *To effectively understand language and safely communicate
> with humans, machines must not only grasp the surface meanings of texts,
> but also their underlying social meaning. This requires understanding
> interpersonal social commonsense, such as knowing to thank someone for
> giving you a present, as well as understanding harmful social biases and
> stereotypes. Failure to account for these social and power dynamics could
> cause models to produce redundant, rude, or even harmful outputs.
>
> In this talk, I will describe my research on enabling machines to reason
> about social dynamics and social biases in text. I will first discuss
> ATOMIC, the first large-scale knowledge graph of social and interpersonal
> commonsense knowledge, with which machines can be taught to reason about
> the causes and effects of everyday events. Then, I will show how we can
> make machines understand and mitigate social biases in language, using
> Social Bias Frames, a new structured formalism for distilling biased
> implications of language, and PowerTransformer, a new unsupervised model
> for controllable debiasing of text.
>
> I will conclude with future research directions on making NLP systems more
> socially-aware and equitable, and how to use language technologies for
> positive societal impact.
>
> *Bio: *Maarten Sap is a final year PhD student in the University of
> Washington's natural language processing (NLP) group, advised by Noah Smith
> and Yejin Choi. His research focuses on making NLP systems socially
> intelligent, and understanding social inequality and bias in language. He
> has presented his work in top-tier NLP and AI conferences, receiving a best
> short paper nomination at ACL 2019 and a best paper award at the WeCNLP
> 2020 summit. Additionally, he and his team won the inaugural Amazon Alexa
> Prize, a social chatbot competition. In the past, he has interned at the
> Allen Institute for AI working on social commonsense reasoning, and at
> Microsoft Research working on deep learning models for understanding human
> cognition.
>
> *Host:* Karen Livescu <klivescu at ttic.edu>
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *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 Wed, Feb 17, 2021 at 6:27 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:*      Thursday, February 18th at* 11:10 am CT*
>>
>>
>>
>> *Where:*     Zoom Virtual Talk (*register in advance here
>> <https://uchicagogroup.zoom.us/webinar/register/WN_FazOKn7bRGq8UdxYLJta_g>*
>> )
>>
>>
>>
>> *Who: *       Maarten Sap, University of Washington
>>
>>
>> *Title:        *Positive AI with Social Commonsense Models
>>
>> *Abstract: *To effectively understand language and safely communicate
>> with humans, machines must not only grasp the surface meanings of texts,
>> but also their underlying social meaning. This requires understanding
>> interpersonal social commonsense, such as knowing to thank someone for
>> giving you a present, as well as understanding harmful social biases and
>> stereotypes. Failure to account for these social and power dynamics could
>> cause models to produce redundant, rude, or even harmful outputs.
>>
>> In this talk, I will describe my research on enabling machines to reason
>> about social dynamics and social biases in text. I will first discuss
>> ATOMIC, the first large-scale knowledge graph of social and interpersonal
>> commonsense knowledge, with which machines can be taught to reason about
>> the causes and effects of everyday events. Then, I will show how we can
>> make machines understand and mitigate social biases in language, using
>> Social Bias Frames, a new structured formalism for distilling biased
>> implications of language, and PowerTransformer, a new unsupervised model
>> for controllable debiasing of text.
>>
>> I will conclude with future research directions on making NLP systems
>> more socially-aware and equitable, and how to use language technologies for
>> positive societal impact.
>>
>> *Bio: *Maarten Sap is a final year PhD student in the University of
>> Washington's natural language processing (NLP) group, advised by Noah Smith
>> and Yejin Choi. His research focuses on making NLP systems socially
>> intelligent, and understanding social inequality and bias in language. He
>> has presented his work in top-tier NLP and AI conferences, receiving a best
>> short paper nomination at ACL 2019 and a best paper award at the WeCNLP
>> 2020 summit. Additionally, he and his team won the inaugural Amazon Alexa
>> Prize, a social chatbot competition. In the past, he has interned at the
>> Allen Institute for AI working on social commonsense reasoning, and at
>> Microsoft Research working on deep learning models for understanding human
>> cognition.
>>
>> *Host:* Karen Livescu <klivescu at ttic.edu>
>>
>>
>> Mary C. Marre
>> Faculty Administrative Support
>> *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 Fri, Feb 12, 2021 at 8:48 AM Mary Marre <mmarre at ttic.edu> wrote:
>>
>>> *When:*      Thursday, February 18th at* 11:10 am CT*
>>>
>>>
>>>
>>> *Where:*     Zoom Virtual Talk (*register in advance here
>>> <https://uchicagogroup.zoom.us/webinar/register/WN_FazOKn7bRGq8UdxYLJta_g>*
>>> )
>>>
>>>
>>>
>>> *Who: *       Maarten Sap, University of Washington
>>>
>>>
>>> *Title:        *Positive AI with Social Commonsense Models
>>>
>>> *Abstract:*
>>> To effectively understand language and safely communicate with humans,
>>> machines must not only grasp the surface meanings of texts, but also their
>>> underlying social meaning. This requires understanding interpersonal social
>>> commonsense, such as knowing to thank someone for giving you a present, as
>>> well as understanding harmful social biases and stereotypes. Failure to
>>> account for these social and power dynamics could cause models to produce
>>> redundant, rude, or even harmful outputs.
>>>
>>> In this talk, I will describe my research on enabling machines to reason
>>> about social dynamics and social biases in text. I will first discuss
>>> ATOMIC, the first large-scale knowledge graph of social and interpersonal
>>> commonsense knowledge, with which machines can be taught to reason about
>>> the causes and effects of everyday events. Then, I will show how we can
>>> make machines understand and mitigate social biases in language, using
>>> Social Bias Frames, a new structured formalism for distilling biased
>>> implications of language, and PowerTransformer, a new unsupervised model
>>> for controllable debiasing of text.
>>>
>>> I will conclude with future research directions on making NLP systems
>>> more socially-aware and equitable, and how to use language technologies for
>>> positive societal impact.
>>>
>>> *Bio:*
>>> Maarten Sap is a final year PhD student in the University of
>>> Washington's natural language processing (NLP) group, advised by Noah Smith
>>> and Yejin Choi. His research focuses on making NLP systems socially
>>> intelligent, and understanding social inequality and bias in language. He
>>> has presented his work in top-tier NLP and AI conferences, receiving a best
>>> short paper nomination at ACL 2019 and a best paper award at the WeCNLP
>>> 2020 summit. Additionally, he and his team won the inaugural Amazon Alexa
>>> Prize, a social chatbot competition. In the past, he has interned at the
>>> Allen Institute for AI working on social commonsense reasoning, and at
>>> Microsoft Research working on deep learning models for understanding human
>>> cognition.
>>>
>>> *Host:* Karen Livescu <klivescu at ttic.edu>
>>>
>>>
>>> Mary C. Marre
>>> Faculty Administrative Support
>>> *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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