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NDS

Available via

LIVESTREAM

Natural Language, Dialog and Speech (NDS) Symposium

Friday, November 22, 2019, 9:00 AM - 6:00 PM

The New York Academy of Sciences, 7 World Trade Center, 250 Greenwich St Fl 40, New York

Presented By

The New York Academy of Sciences

 

Natural language, dialog and speech (NDS) researchers focus on communication between people and computers using human languages both in written and spoken forms. They develop models for analyzing the structure and content of human conversation and create artificial agents who can engage in human-like interaction with people and other agents.

Building on the success of NYAS’s annual Machine Learning Symposium, the longest-running conference on Machine Learning in the Eastern United States, NDS2019 will convene leading researchers from academia and industry to discuss cutting-edge methodologies and computational approaches to applied and theoretical problems in dialog systems, spoken and natural language understanding, natural language generation and speech synthesis.

Livestreams:  Two of the Keynotes are available via Livestream:

Keynote #1 (Commonsense Intelligence: Cracking the Longstanding Challenge in AI, Yejin Choi, University of Washington) at 10:10 am

Keynote #2 (Propagation, Persuasion, and Polarization: Language Effectiveness and Effects of Language, Lillian Lee, Cornell University) at 12:20 pm 

Registration

Member
By 10/18/2019
$90
After 10/18/2019
$130
Nonmember Academia, Faculty, etc.
By 10/18/2019
$180
After 10/18/2019
$260
Nonmember Corporate, Other
By 10/18/2019
$250
After 10/18/2019
$350
Nonmember Not for Profit
By 10/18/2019
$180
After 10/18/2019
$260
Nonmember Student, Undergrad, Grad, Fellow
By 10/18/2019
$100
After 10/18/2019
$145
Member Student, Post-Doc, Fellow
By 10/18/2019
$50
After 10/18/2019
$70
Earlybird Registration:
0
days
left
Deadline:
0
days
left

Keynote Speakers

Karen Livescu, PhD
Karen Livescu, PhD

Toyota Technological Institute at Chicago

Lillian Lee, PhD
Lillian Lee, PhD

Cornell University

Yejin Choi
Yejin Choi, PhD

University of Washington

Organizing Committee

Sam Bowman
Sam Bowman, PhD

New York University

Jennifer Costley
Jennifer Costley, PhD

The New York Academy of Sciences

Rivka Levitan
Rivka Levitan, PhD

Brooklyn College & The Graduate Center, CUNY

Lidia Mangu
Lidia Mangu, PhD

JP Morgan Chase

Smaranda Muresan
Smaranda Muresan, PhD

Columbia University

Event Sponsors





Program Support

Physical Sciences, Sustainability & Engineering Lead Supporters

JP Morgan Chase

Friday

November 22, 2019

9:00 AM

Registration, Continental Breakfast, and Poster Set-up

10:00 AM

Welcome Remarks

Keynote Address 1

10:10 AM

Commonsense Intelligence: Cracking the Longstanding Challenge in AI

Speaker

Yejin Choi
University of Washington

Despite considerable advances in deep learning, AI remains to be narrow and brittle. One fundamental limitation comes from its lack of commonsense intelligence: reasoning about everyday situations and events, which in turn, requires knowledge about how the physical and social world works. In this talk, I will share some of our recent efforts that attempt to crack commonsense intelligence.

First, I will introduce ATOMIC, the atlas of everyday commonsense knowledge and reasoning, organized as a graph of 877k if-then rules (e.g., "if X pays Y a compliment, then Y will likely return the compliment”). Next, I will introduce COMET, our deep neural networks that can learn from and generalize beyond the ATOMIC commonsense graph. Finally, I will present RAINBOW, a collection of seven benchmarks that aims to cover a wide spectrum of commonsense intelligence from natural language inference to adductive reasoning to visual commonsense reasoning. I will conclude the talk by discussing major open research questions, including the importance of algorithmic solutions to reduce incidental biases in data that can lead to overestimation of true AI capabilities.

10:50 AM

Audience Q&A

STAR Talks: Session 1

11:05 AM

Multimodal Dialogue for Interacting with Data

Speaker

Malihe Alikhani
Rutgers University
11:10 AM

Automatic Extraction of Polysemous Words from Contextualized Embeddings

Speaker

Leslie Huang
New York University
11:15 AM

A Good Sample is Hard to Find: Noise Injection Sampling and Self-Training for Neural Language Generation Models

Speaker

Chris Kedzie
Columbia University
11:20 AM

Deciphering How People Detect Lies: Acoustic-Prosodic and Lexical Cues to Deception and Trust

Speaker

Sarah Ita Levitan
Columbia University
11:25 AM

Finding Generalizable Evidence by Learning to Convince Q&A Models

Speaker

Ethan Perez
New York University
11:30 AM

Networking Break and Poster Viewing

Keynote Address 2

12:20 PM

Propagation, Persuasion, and Polarization: Language Effectiveness and Effects of Language

Speaker

Lillian Lee
Cornell University

Does the way in which something is worded in and of itself have an effect on whether it is remembered or attracts attention, perhaps beyond its content or context? We'll present work using language analysis to predict memorable movie quotes, persuasive arguments in the community ChangeMyView, and controversial comments on Reddit.

1:00 PM

Audience Q&A

1:15 PM

Networking Lunch and Poster Viewing

STAR Talks: Session 2

2:30 PM

Temporally Aware Named Entity Recognition

Speaker

Shruti Rijhwani
Carnegie Mellon University
2:35 PM

Understanding Learning Dynamics of Language Models with SVCCA

Speaker

Naomi Saphra
University of Edinburgh
2:40 PM

Evaluating Conversational Agents

Speaker

João Sedoc
Johns Hopkins University
2:45 PM

Executing Instructions in Situated Collaborative Interactions

Speaker

Alane Suhr
Cornell University
2:50 PM

Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions

Speaker

Rui Zhang
Yale University

Keynote Address 3

2:55 PM

Embeddings for Spoken Words

Speaker

Karen Livescu
Toyota Technological Institute at Chicago

Word embeddings have become a ubiquitous tool in natural language processing. These embeddings represent the meanings of written words. On the other hand, for spoken language it may be more important to represent how a written word *sounds* rather than (or in addition to) what it means. For some applications it can also be helpful to represent variable-length acoustic segments corresponding to words, or other linguistic units, as fixed-dimensional vectors. This talk will present recent work on both acoustic word embeddings and "acoustically grounded" written word embeddings, including their applications for improved speech recognition and search.

3:35 PM

Audience Q&A

3:50 PM

Networking Break

Keynote Address 4

4:05 PM

Towards Large-Scale Federated Conversational Intelligence

Speaker

Sungjin Lee
Amazon

Conversational agents have become prevalent in every aspect of our lives. Conversational agents such as Alexa and Google Assistant are no longer closed applications but rather they have evolved to be an ecosystem featuring hundreds of thousands of voice skills and offer a rich set of tools to bring in unlimited number of skills, for instance, an AI-centric toolkit for voice skill authoring, seamless cold start of new skills, and traffic optimization based on user satisfaction. In this talk, I discuss recent industrial trends towards large-scale federated conversational intelligence and give a sneak peek of present challenges and approaches in industry.

4:45 PM

Audience Q&A

5:00 PM

Closing Remarks and Awards (Best Poster Presentation, STAR Talk Award Presentation)

5:10 PM

Networking Reception

6:00 PM

Symposium Adjourns

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