[Theory] REMINDER: 3/24 Talks at TTIC: Arman Cohan, Allen Institute for AI (AI2) and University of Washington

[Mary Marre]

*When:*        Thursday, March 24th at* 11:00 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_nXAsFqgjSRCfQU0EYNiFGQ>*)


*Who: *         Arman Cohan, Allen Institute for AI (AI2) and University of
Washington




*Title:          *Beyond Sentences and Paragraphs: Towards Document and
Multi-document Understanding

*Abstract: *During the past few years, there has been significant progress
in natural language understanding, primarily due to the advancements in
transfer learning methods and the increasing scale of pre-trained language
models. However, the majority of progress has been made on tasks concerning
short texts with sentences or paragraphs as the basic unit of analysis.
Yet, many real-world natural language tasks require understanding full
documents which includes learning effective representation of documents,
resolving longer range dependencies, structure, and argumentation. Further,
certain tasks require incorporating additional context from multiple
related documents (e.g., understanding a scientific paper) and aggregating
information across multiple documents. In this talk, I will discuss some of
our recent works on addressing these challenges. I will first discuss
general methods for document representation learning that help to achieve
strong downstream performance on a variety of document-level tasks. Then I
will focus on how we can have a general pre-trained language model that can
process long documents. Using this framework, I will discuss extensions to
multi-document natural language understanding for a variety of
classification, extraction, and summarization tasks. I will also briefly
discuss a few of our newly developed benchmarks from challenging domains
that enable us to better measure progress on document natural language
understanding.

*Bio: *Arman Cohan is a Research Scientist at the Allen Institute for AI
(AI2) and an Affiliate Assistant Professor at the University of Washington.
His broad research interest is developing natural language processing (NLP)
methods for addressing information overload. This includes models and
benchmarks for document and multi-document understanding, natural language
generation and summarization, as well as information discovery and
filtering. He is additionally interested in real-world interdisciplinary
applications of NLP in the science and health domains. His research has
been recognized with multiple awards, including a best paper award at EMNLP
2017, an honorable mention at COLING 2018, and the 2019 Harold N. Glassman
Distinguished Doctoral Dissertation award.

*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 23, 2022 at 2:58 PM Mary Marre <mmarre at ttic.edu> wrote:

> *When:*        Thursday, March 24th at* 11:00 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_nXAsFqgjSRCfQU0EYNiFGQ>*
> )
>
>
> *Who: *         Arman Cohan, Allen Institute for AI (AI2) and University
> of Washington
>
>
>
>
> *Title:          *Beyond Sentences and Paragraphs: Towards Document and
> Multi-document Understanding
>
> *Abstract: *During the past few years, there has been significant
> progress in natural language understanding, primarily due to the
> advancements in transfer learning methods and the increasing scale of
> pre-trained language models. However, the majority of progress has been
> made on tasks concerning short texts with sentences or paragraphs as the
> basic unit of analysis. Yet, many real-world natural language tasks require
> understanding full documents which includes learning effective
> representation of documents, resolving longer range dependencies,
> structure, and argumentation. Further, certain tasks require incorporating
> additional context from multiple related documents (e.g., understanding a
> scientific paper) and aggregating information across multiple documents. In
> this talk, I will discuss some of our recent works on addressing these
> challenges. I will first discuss general methods for document
> representation learning that help to achieve strong downstream performance
> on a variety of document-level tasks. Then I will focus on how we can have
> a general pre-trained language model that can process long documents. Using
> this framework, I will discuss extensions to multi-document natural
> language understanding for a variety of classification, extraction, and
> summarization tasks. I will also briefly discuss a few of our newly
> developed benchmarks from challenging domains that enable us to better
> measure progress on document natural language understanding.
>
> *Bio: *Arman Cohan is a Research Scientist at the Allen Institute for AI
> (AI2) and an Affiliate Assistant Professor at the University of Washington.
> His broad research interest is developing natural language processing (NLP)
> methods for addressing information overload. This includes models and
> benchmarks for document and multi-document understanding, natural language
> generation and summarization, as well as information discovery and
> filtering. He is additionally interested in real-world interdisciplinary
> applications of NLP in the science and health domains. His research has
> been recognized with multiple awards, including a best paper award at EMNLP
> 2017, an honorable mention at COLING 2018, and the 2019 Harold N. Glassman
> Distinguished Doctoral Dissertation award.
>
> *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 Fri, Mar 18, 2022 at 9:59 AM Mary Marre <mmarre at ttic.edu> wrote:
>
>> *When:*        Thursday, March 24th at* 11:00 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_nXAsFqgjSRCfQU0EYNiFGQ>*
>> )
>>
>>
>> *Who: *         Arman Cohan, Allen Institute for AI (AI2) and University
>> of Washington
>>
>>
>>
>>
>> *Title:          *Beyond Sentences and Paragraphs: Towards Document and
>> Multi-document Understanding
>>
>> *Abstract: *During the past few years, there has been significant
>> progress in natural language understanding, primarily due to the
>> advancements in transfer learning methods and the increasing scale of
>> pre-trained language models. However, the majority of progress has been
>> made on tasks concerning short texts with sentences or paragraphs as the
>> basic unit of analysis. Yet, many real-world natural language tasks require
>> understanding full documents which includes learning effective
>> representation of documents, resolving longer range dependencies,
>> structure, and argumentation. Further, certain tasks require incorporating
>> additional context from multiple related documents (e.g., understanding a
>> scientific paper) and aggregating information across multiple documents. In
>> this talk, I will discuss some of our recent works on addressing these
>> challenges. I will first discuss general methods for document
>> representation learning that help to achieve strong downstream performance
>> on a variety of document-level tasks. Then I will focus on how we can have
>> a general pre-trained language model that can process long documents. Using
>> this framework, I will discuss extensions to multi-document natural
>> language understanding for a variety of classification, extraction, and
>> summarization tasks. I will also briefly discuss a few of our newly
>> developed benchmarks from challenging domains that enable us to better
>> measure progress on document natural language understanding.
>>
>> *Bio: *Arman Cohan is a Research Scientist at the Allen Institute for AI
>> (AI2) and an Affiliate Assistant Professor at the University of Washington.
>> His broad research interest is developing natural language processing (NLP)
>> methods for addressing information overload. This includes models and
>> benchmarks for document and multi-document understanding, natural language
>> generation and summarization, as well as information discovery and
>> filtering. He is additionally interested in real-world interdisciplinary
>> applications of NLP in the science and health domains. His research has
>> been recognized with multiple awards, including a best paper award at EMNLP
>> 2017, an honorable mention at COLING 2018, and the 2019 Harold N. Glassman
>> Distinguished Doctoral Dissertation award.
>>
>> *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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