[Colloquium] NOW: 3/22 NLP Seminar: Zhewei Sun, University of Toronto
Mary Marre
mmarre at ttic.edu
Fri Mar 22 11:04:53 CDT 2024
*When:* Friday, March 22, 2024 at* 11:00** a**m CT *
*Where: *Talk will be given *live, in-person* at
TTIC, 6045 S. Kenwood Avenue
5th Floor, Room 529
*Virtually:* *via **zoom*
<https://uchicago.zoom.us/j/98297764499?pwd=ajNQSTZnMHRmMENkd1hjdjlNeW1xdz09>
*Who: * Zhewei Sun, University of Toronto
------------------------------
*Title: *Contextualizing Natural Language Agents: The Case of Slang
*Abstract: *Large language models (LLMs) have recently emerged as an
effective tool for enhancing human productivity. Although conversational
artificial intelligence (AI) agents such as ChatGPT produces natural
language that is much more human-like compared to its predecessors, they
still lack the ability to situate themselves in a communicative context
like humans do. For instance, humans may opt to use informal language such
as slang in a conversation to express a close bond with the other party. In
this talk, I will illustrate how natural language processing (NLP)
techniques can enable automatic processing of slang. The first part of my
talk will cover knowledge-driven approaches that inject linguistic and
cognitive knowledge about slang into foundational NLP models for (1)
generation, (2) interpretation, and (3) modeling slang semantic variation.
Next, I will discuss emerging data-driven approaches based on large
language models, examining the extent of knowledge LLMs have acquired about
slang and how such knowledge may have been obtained. Finally, I will
discuss potential future directions to further enhance an LLM’s ability to
process contextualized language.
*Bio: *Zhewei Sun is a Ph.D. Candidate in the Department of Computer
Science at the University of Toronto, working with Professor Yang Xu. His
research is focused on natural language processing (NLP) of informal
language, with a particular interest in the automatic processing of slang.
His research explores both data-driven NLP approaches based on deep
learning, as well as knowledge-driven approaches that combine traditional
NLP techniques with linguistic and cognitive knowledge about human
language. His work has been recognized at top-tier NLP venues, including
EMNLP, NAACL, and TACL. Zhewei’s work has been supported in part by Amazon
Alexa AI and the Queen Elizabeth II Graduate Scholarship in Science and
Technology. Prior to joining the University of Toronto, Zhewei received an
M.Sc. in Computer Science from Georgia Institute of Technology and a B.Sc.
in Computer Science from the University of Waterloo.
*Host: **Jiawei Zhou* <jzhou at ttic.edu>
Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue, Rm 517*
*Chicago, IL 60637*
*773-834-1757*
*mmarre at ttic.edu <mmarre at ttic.edu>*
On Fri, Mar 22, 2024 at 10:29 AM Mary Marre <mmarre at ttic.edu> wrote:
> *When:* Friday, March 22, 2024 at* 11:00** a**m CT *
>
>
> *Where: *Talk will be given *live, in-person* at
>
> TTIC, 6045 S. Kenwood Avenue
>
> 5th Floor, Room 529
>
>
> *Virtually:* *via **zoom*
> <https://uchicago.zoom.us/j/98297764499?pwd=ajNQSTZnMHRmMENkd1hjdjlNeW1xdz09>
>
>
>
>
>
> *Who: * Zhewei Sun, University of Toronto
> ------------------------------
> *Title: *Contextualizing Natural Language Agents: The Case of
> Slang
>
> *Abstract: *Large language models (LLMs) have recently emerged as an
> effective tool for enhancing human productivity. Although conversational
> artificial intelligence (AI) agents such as ChatGPT produces natural
> language that is much more human-like compared to its predecessors, they
> still lack the ability to situate themselves in a communicative context
> like humans do. For instance, humans may opt to use informal language such
> as slang in a conversation to express a close bond with the other party. In
> this talk, I will illustrate how natural language processing (NLP)
> techniques can enable automatic processing of slang. The first part of my
> talk will cover knowledge-driven approaches that inject linguistic and
> cognitive knowledge about slang into foundational NLP models for (1)
> generation, (2) interpretation, and (3) modeling slang semantic variation.
> Next, I will discuss emerging data-driven approaches based on large
> language models, examining the extent of knowledge LLMs have acquired about
> slang and how such knowledge may have been obtained. Finally, I will
> discuss potential future directions to further enhance an LLM’s ability to
> process contextualized language.
>
> *Bio: *Zhewei Sun is a Ph.D. Candidate in the Department of Computer
> Science at the University of Toronto, working with Professor Yang Xu. His
> research is focused on natural language processing (NLP) of informal
> language, with a particular interest in the automatic processing of slang.
> His research explores both data-driven NLP approaches based on deep
> learning, as well as knowledge-driven approaches that combine traditional
> NLP techniques with linguistic and cognitive knowledge about human
> language. His work has been recognized at top-tier NLP venues, including
> EMNLP, NAACL, and TACL. Zhewei’s work has been supported in part by Amazon
> Alexa AI and the Queen Elizabeth II Graduate Scholarship in Science and
> Technology. Prior to joining the University of Toronto, Zhewei received an
> M.Sc. in Computer Science from Georgia Institute of Technology and a B.Sc.
> in Computer Science from the University of Waterloo.
>
>
> *Host: **Jiawei Zhou* <jzhou at ttic.edu>
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue, Rm 517*
> *Chicago, IL 60637*
> *773-834-1757*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
>
> On Thu, Mar 21, 2024 at 4:50 PM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:* Friday, March 22, 2024 at* 11:00** a**m CT *
>>
>>
>> *Where: *Talk will be given *live, in-person* at
>>
>> TTIC, 6045 S. Kenwood Avenue
>>
>> 5th Floor, Room 529
>>
>>
>> *Virtually:* *via **zoom*
>> <https://uchicago.zoom.us/j/98297764499?pwd=ajNQSTZnMHRmMENkd1hjdjlNeW1xdz09>
>>
>>
>>
>>
>>
>> *Who: * Zhewei Sun, University of Toronto
>> ------------------------------
>> *Title: *Contextualizing Natural Language Agents: The Case of
>> Slang
>>
>> *Abstract: *Large language models (LLMs) have recently emerged as an
>> effective tool for enhancing human productivity. Although conversational
>> artificial intelligence (AI) agents such as ChatGPT produces natural
>> language that is much more human-like compared to its predecessors, they
>> still lack the ability to situate themselves in a communicative context
>> like humans do. For instance, humans may opt to use informal language such
>> as slang in a conversation to express a close bond with the other party. In
>> this talk, I will illustrate how natural language processing (NLP)
>> techniques can enable automatic processing of slang. The first part of my
>> talk will cover knowledge-driven approaches that inject linguistic and
>> cognitive knowledge about slang into foundational NLP models for (1)
>> generation, (2) interpretation, and (3) modeling slang semantic variation.
>> Next, I will discuss emerging data-driven approaches based on large
>> language models, examining the extent of knowledge LLMs have acquired about
>> slang and how such knowledge may have been obtained. Finally, I will
>> discuss potential future directions to further enhance an LLM’s ability to
>> process contextualized language.
>>
>> *Bio: *Zhewei Sun is a Ph.D. Candidate in the Department of Computer
>> Science at the University of Toronto, working with Professor Yang Xu. His
>> research is focused on natural language processing (NLP) of informal
>> language, with a particular interest in the automatic processing of slang.
>> His research explores both data-driven NLP approaches based on deep
>> learning, as well as knowledge-driven approaches that combine traditional
>> NLP techniques with linguistic and cognitive knowledge about human
>> language. His work has been recognized at top-tier NLP venues, including
>> EMNLP, NAACL, and TACL. Zhewei’s work has been supported in part by Amazon
>> Alexa AI and the Queen Elizabeth II Graduate Scholarship in Science and
>> Technology. Prior to joining the University of Toronto, Zhewei received an
>> M.Sc. in Computer Science from Georgia Institute of Technology and a B.Sc.
>> in Computer Science from the University of Waterloo.
>>
>>
>> *Host: **Jiawei Zhou* <jzhou at ttic.edu>
>>
>>
>>
>> Mary C. Marre
>> Faculty Administrative Support
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue, Rm 517*
>> *Chicago, IL 60637*
>> *773-834-1757*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>
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