[Colloquium] Re: REMINDER: 2/8 Talks at TTIC: Ellie Pavlick, University of Pennsylvania

Wed Feb 8 10:38:12 CST 2017

When:     Wednesday, February 8th at 11:00 am

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

Who:       Ellie Pavlick, University of Pennsylvania

Title: Natural Language Understanding with Paraphrases and Composition

Abstract: Natural language processing (NLP) aims to teach computers to
understand human language. NLP has enabled some of the most visible
applications of artificial intelligence, including Google search, IBM
Watson, and Apple’s Siri. As AI is applied to increasingly complex domains
such as health care, education, and government, NLP will play a crucial
role in allowing computational systems to access the vast amount of human
knowledge documented in the form of unstructured speech and text. In this
talk, I will discuss my work on training computers to make inferences about
what is true or false based on information expressed in natural language.
My approach combines machine learning with insights from formal linguistics
in order to build data-driven models of semantics which are more precise
and interpretable than would be possible using linguistically naive
approaches. I will begin with my work on automatically adding semantic
annotations to the 100 million phrase pairs in the Paraphrase Database
(PPDB). These annotations provide the type of information necessary for
carrying out precise inferences in natural language, transforming the
database into a largest available lexical semantics resource for natural
language processing. I will then turn to the problem of compositional
entailment, and present an algorithm for performing inferences about long
phrases which are unlikely to have been observed in data. Finally, I will
discuss my current work on pragmatic reasoning: when and how humans derive
meaning from a sentence beyond what is literally contained in the words. I
will describe the difficulties that such "common-sense" inference poses for
automatic language understanding, and present my on-going work on models
for overcoming these challenges. Bio: Ellie Pavlick is a PhD student at the
University of Pennsylvania, advised by Dr. Chris Callison-Burch. Her
dissertation focuses on natural language inference and entailment. Outside
of her dissertation research, Ellie has published work on stylistic
variation in paraphrase--e.g. how paraphrases can effect the formality or
the complexity of language--and on applications of crowdsourcing to natural
language processing and social science problems. She has been involved in
the design and instruction of Penn's first undergraduate course on
Crowdsourcing and Human Computation (NETS 213). Ellie is a 2016 Facebook
PhD Fellow, and has interned at Google Research, Yahoo Labs, and the Allen
Institute for Artificial Intelligence.

Host: Kevin Gimpel <kgimpel at ttic.edu>

Mary C. Marre
Administrative Assistant
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Room 504*
*Chicago, IL  60637*
*p:(773) 834-1757*
*f: (773) 357-6970*
*mmarre at ttic.edu <mmarre at ttic.edu>*

On Tue, Feb 7, 2017 at 3:19 PM, Mary Marre <mmarre at ttic.edu> wrote:

> When:     Wednesday, February 8th at 11:00 am
>
> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>
> Who:       Ellie Pavlick, University of Pennsylvania
>
>
> Title: Natural Language Understanding with Paraphrases and Composition
>
> Abstract: Natural language processing (NLP) aims to teach computers to
> understand human language. NLP has enabled some of the most visible
> applications of artificial intelligence, including Google search, IBM
> Watson, and Apple’s Siri. As AI is applied to increasingly complex domains
> such as health care, education, and government, NLP will play a crucial
> role in allowing computational systems to access the vast amount of human
> knowledge documented in the form of unstructured speech and text. In this
> talk, I will discuss my work on training computers to make inferences about
> what is true or false based on information expressed in natural language.
> My approach combines machine learning with insights from formal linguistics
> in order to build data-driven models of semantics which are more precise
> and interpretable than would be possible using linguistically naive
> approaches. I will begin with my work on automatically adding semantic
> annotations to the 100 million phrase pairs in the Paraphrase Database
> (PPDB). These annotations provide the type of information necessary for
> carrying out precise inferences in natural language, transforming the
> database into a largest available lexical semantics resource for natural
> language processing. I will then turn to the problem of compositional
> entailment, and present an algorithm for performing inferences about long
> phrases which are unlikely to have been observed in data. Finally, I will
> discuss my current work on pragmatic reasoning: when and how humans derive
> meaning from a sentence beyond what is literally contained in the words. I
> will describe the difficulties that such "common-sense" inference poses for
> automatic language understanding, and present my on-going work on models
> for overcoming these challenges. Bio: Ellie Pavlick is a PhD student at
> the University of Pennsylvania, advised by Dr. Chris Callison-Burch. Her
> dissertation focuses on natural language inference and entailment. Outside
> of her dissertation research, Ellie has published work on stylistic
> variation in paraphrase--e.g. how paraphrases can effect the formality or
> the complexity of language--and on applications of crowdsourcing to natural
> language processing and social science problems. She has been involved in
> the design and instruction of Penn's first undergraduate course on
> Crowdsourcing and Human Computation (NETS 213). Ellie is a 2016 Facebook
> PhD Fellow, and has interned at Google Research, Yahoo Labs, and the Allen
> Institute for Artificial Intelligence.
>
>
> Host: Kevin Gimpel <kgimpel at ttic.edu>
>
>
>
>
>
> Mary C. Marre
> Administrative Assistant
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Room 504*
> *Chicago, IL  60637*
> *p:(773) 834-1757 <(773)%20834-1757>*
> *f: (773) 357-6970 <(773)%20357-6970>*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
> On Wed, Feb 1, 2017 at 10:45 PM, Mary Marre <mmarre at ttic.edu> wrote:
>
>> When:     Wednesday, February 8th at 11:00 am
>>
>> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>>
>> Who:       Ellie Pavlick, University of Pennsylvania
>>
>>
>> Title: Natural Language Understanding with Paraphrases and Composition
>>
>> Abstract: Natural language processing (NLP) aims to teach computers to
>> understand human language. NLP has enabled some of the most visible
>> applications of artificial intelligence, including Google search, IBM
>> Watson, and Apple’s Siri. As AI is applied to increasingly complex domains
>> such as health care, education, and government, NLP will play a crucial
>> role in allowing computational systems to access the vast amount of human
>> knowledge documented in the form of unstructured speech and text. In this
>> talk, I will discuss my work on training computers to make inferences about
>> what is true or false based on information expressed in natural language.
>> My approach combines machine learning with insights from formal linguistics
>> in order to build data-driven models of semantics which are more precise
>> and interpretable than would be possible using linguistically naive
>> approaches. I will begin with my work on automatically adding semantic
>> annotations to the 100 million phrase pairs in the Paraphrase Database
>> (PPDB). These annotations provide the type of information necessary for
>> carrying out precise inferences in natural language, transforming the
>> database into a largest available lexical semantics resource for natural
>> language processing. I will then turn to the problem of compositional
>> entailment, and present an algorithm for performing inferences about long
>> phrases which are unlikely to have been observed in data. Finally, I will
>> discuss my current work on pragmatic reasoning: when and how humans derive
>> meaning from a sentence beyond what is literally contained in the words. I
>> will describe the difficulties that such "common-sense" inference poses for
>> automatic language understanding, and present my on-going work on models
>> for overcoming these challenges. Bio: Ellie Pavlick is a PhD student at the
>> University of Pennsylvania, advised by Dr. Chris Callison-Burch. Her
>> dissertation focuses on natural language inference and entailment. Outside
>> of her dissertation research, Ellie has published work on stylistic
>> variation in paraphrase--e.g. how paraphrases can effect the formality or
>> the complexity of language--and on applications of crowdsourcing to natural
>> language processing and social science problems. She has been involved in
>> the design and instruction of Penn's first undergraduate course on
>> Crowdsourcing and Human Computation (NETS 213). Ellie is a 2016 Facebook
>> PhD Fellow, and has interned at Google Research, Yahoo Labs, and the Allen
>> Institute for Artificial Intelligence.
>>
>>
>> Host: Kevin Gimpel <kgimpel at ttic.edu>
>>
>>
>>
>>
>> Mary C. Marre
>> Administrative Assistant
>> *Toyota Technological Institute*
>> *6045 S. Kenwood Avenue*
>> *Room 504*
>> *Chicago, IL  60637*
>> *p:(773) 834-1757 <(773)%20834-1757>*
>> *f: (773) 357-6970 <(773)%20357-6970>*
>> *mmarre at ttic.edu <mmarre at ttic.edu>*
>>
>
>
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