[Theory] REMINDER: 2/20 Talks at TTIC: Antoine Bosselut, University of Washington

[Mary Marre]

*When:*      Thursday, February 20th at 11:00 am



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



*Who: *        Antoine Bosselut, University of Washington


*Title:    *Neuro-symbolic Representations for Commonsense Knowledge and
Reasoning

*Abstract: *Situations described using natural language are richer than
what humans explicitly communicate. For example, the sentence "She pumped
her fist" connotes many potential auspicious causes. For machines to
understand natural language, they must be able to reason about the
commonsense inferences that underlie explicitly stated information. In this
talk, I will present work on combining traditional symbolic knowledge and
reasoning techniques with modern neural representations to endow machines
with these capacities.

First, I will describe COMET, an approach for learning commonsense
knowledge about unlimited situations and concepts using transfer learning
from language to knowledge. Second, I will demonstrate how these neural
knowledge representations can dynamically construct symbolic graphs of
contextual commonsense knowledge, and how these graphs can be used for
interpretable, generalized reasoning. Finally, I will discuss future
research directions on conceptualizing NLP as commonsense simulation, and
the impact of this framing on difficult open-ended tasks such as story
generation and dialogue.

*Bio: *Antoine Bosselut is a final year PhD Student at the University of
Washington advised by Professor Yejin Choi, and a student researcher at the
Allen Institute for Artificial Intelligence. He was previously a student
researcher on the Deep Learning team at Microsoft Research from 2017 to
2018. His research focuses on building systems for commonsense knowledge
representation and reasoning that combine the strengths of modern neural
and traditional symbolic methods. He was the recipient of an AI2 Key
Scientific Challenges award in 2018.


*Host:* *Kevin Gimpel* <kgimpel 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 19, 2020 at 1:11 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*      Thursday, February 20th at 11:00 am
>
>
>
> *Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>
>
>
> *Who: *        Antoine Bosselut, University of Washington
>
>
> *Title:    *Neuro-symbolic Representations for Commonsense Knowledge and
> Reasoning
>
> *Abstract: *Situations described using natural language are richer than
> what humans explicitly communicate. For example, the sentence "She pumped
> her fist" connotes many potential auspicious causes. For machines to
> understand natural language, they must be able to reason about the
> commonsense inferences that underlie explicitly stated information. In this
> talk, I will present work on combining traditional symbolic knowledge and
> reasoning techniques with modern neural representations to endow machines
> with these capacities.
>
> First, I will describe COMET, an approach for learning commonsense
> knowledge about unlimited situations and concepts using transfer learning
> from language to knowledge. Second, I will demonstrate how these neural
> knowledge representations can dynamically construct symbolic graphs of
> contextual commonsense knowledge, and how these graphs can be used for
> interpretable, generalized reasoning. Finally, I will discuss future
> research directions on conceptualizing NLP as commonsense simulation, and
> the impact of this framing on difficult open-ended tasks such as story
> generation and dialogue.
>
> *Bio: *Antoine Bosselut is a final year PhD Student at the University of
> Washington advised by Professor Yejin Choi, and a student researcher at the
> Allen Institute for Artificial Intelligence. He was previously a student
> researcher on the Deep Learning team at Microsoft Research from 2017 to
> 2018. His research focuses on building systems for commonsense knowledge
> representation and reasoning that combine the strengths of modern neural
> and traditional symbolic methods. He was the recipient of an AI2 Key
> Scientific Challenges award in 2018.
>
>
> *Host:* *Kevin Gimpel* <kgimpel 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 13, 2020 at 3:46 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:*      Thursday, February 20th at 11:00 am
>>
>>
>>
>> *Where:*     TTIC, 6045 S. Kenwood Avenue, 5th Floor, Room 526
>>
>>
>>
>> *Who: *        Antoine Bosselut, University of Washington
>>
>>
>> *Title:    *Neuro-symbolic Representations for Commonsense Knowledge and
>> Reasoning
>>
>> *Abstract: *Situations described using natural language are richer than
>> what humans explicitly communicate. For example, the sentence "She pumped
>> her fist" connotes many potential auspicious causes. For machines to
>> understand natural language, they must be able to reason about the
>> commonsense inferences that underlie explicitly stated information. In this
>> talk, I will present work on combining traditional symbolic knowledge and
>> reasoning techniques with modern neural representations to endow machines
>> with these capacities.
>>
>> First, I will describe COMET, an approach for learning commonsense
>> knowledge about unlimited situations and concepts using transfer learning
>> from language to knowledge. Second, I will demonstrate how these neural
>> knowledge representations can dynamically construct symbolic graphs of
>> contextual commonsense knowledge, and how these graphs can be used for
>> interpretable, generalized reasoning. Finally, I will discuss future
>> research directions on conceptualizing NLP as commonsense simulation, and
>> the impact of this framing on difficult open-ended tasks such as story
>> generation and dialogue.
>>
>> *Bio: *Antoine Bosselut is a final year PhD Student at the University of
>> Washington advised by Professor Yejin Choi, and a student researcher at the
>> Allen Institute for Artificial Intelligence. He was previously a student
>> researcher on the Deep Learning team at Microsoft Research from 2017 to
>> 2018. His research focuses on building systems for commonsense knowledge
>> representation and reasoning that combine the strengths of modern neural
>> and traditional symbolic methods. He was the recipient of an AI2 Key
>> Scientific Challenges award in 2018.
>>
>>
>> *Host:* *Kevin Gimpel* <kgimpel 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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