NATURAL LANGUAGE PROCESSING (NLP) SYMPOSIUM
September 16 @ 10:30 am - 1:30 pm
Natural Language Symposium
September 15 & 16, 2020
10:00 am – 1:30 pm EST
The Vector Institute is hosting a Natural Language Processing (NLP) Symposium showcasing the NLP project with academic-industry collaborators to facilitate interaction between our industry sponsors, researchers, students and faculty members.
In June 2019, Vector Institute launched a multi-phase industry-academic collaborative project focusing on recent advances in NLP. Participants replicated a state-of-the-art NLP model called BERT and fine tuned a transfer learning approach to optimize domain-specific tasks in areas such as health, law and finance.
To follow-up on the outcomes of the project, a two-day symposium will be held featuring presentations and hands-on workshops, delivered by the project participants and Vector researchers.
The symposium will support knowledge transfer and provide an exclusive opportunity for Vector’s industry sponsors to engage with talent in the NLP domain.
Level of workshops: Beginner/Intermediate
Required skill set: Fundamentals of machine learning and deep learning; knowledge of Language modelling and/or transformers; experience programming in Python and any of the deep learning frameworks (Tensorflow, Pytorch); experience using GPUs for accelerated deep learning training; experience in using jupyter notebook and/or Google Colab.
** Participants must be individuals actively involved in NLP research and/or development*
September 16, 2020:
(As of July 30, 2020)
10:00 am – 10:10 am Opening Remarks
Cameron Schuler, Chief Commercialization Officer and VP, Industry Innovation, Vector Institute
10:10 am – 10:40 am Keynote Presentation
Speaker to be announced
10:40 am – 11:00 am
Act a demon supporter is a natural process of playing with words burst the master
In this talk, I will “play the devil’s advocate and burst the natural language processing bubble” by highlighting certain failures, inconsistencies, controversies, and uncertainties about the field, as it stands. This will come from a place of love, since we need to be aware of challenges, as deployments increase.
Speaker: Frank Rudzicz, Scientist, International Centre for Surgical Safety, Li Ka Shing Institute, St. Michael’s Hospital, Associate Professor Department of Computer Science, University of Toronto, Director of AI, Surgical Safety Technologies Inc., Co-Founder, WinterLight Labs Inc., Faculty Member, Vector Institute, Canada CIFAR Artificial Intelligence Chair
11:00 am – 11:30 am
Speakers to be announced
11:30 am – 12:00 noon
Panel Discussion: Business Impacts:
What is the main risk to NLP or from NLP in the next 5 years?”
Moderator: Frank Rudzicz, Scientist, International Centre for Surgical Safety, Li Ka Shing Institute, St. Michael’s Hospital, Associate Professor Department of Computer Science, University of Toronto, Director of AI, Surgical Safety Technologies Inc., Co-Founder, WinterLight Labs Inc., Faculty Member, Vector Institute, Canada CIFAR Artificial Intelligence Chair
Panelists: Stephany Lapierre, CEO, Tealbook
Yevgeniy Vahlis, Head of Artificial Intelligence Capabilities, BMO Financial Group
Ozge Yeloglu, VP Enterprise Advanced Analytics, CIBC
12 noon – 12:30 pm
Networking and Poster Session
Poster Presenters to be announced
12:30 pm – 1:30 pm
WS1: How to use Fairseq Facebook AI Research Sequence-to-Sequence Toolkit
Fairseq library is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. In this short workshop we will walk participants through the basics of the Fairseq library. We will dive into their codebase and learn how to modify existing modules to create and keep track of new applications. The purpose of this workshop is to provide learning through demonstration and hands-on experience.
Facilitators: Joey Cheng, Machine Learning Research Scientist, Layer 6
Gary Huang, Machine Learning Research Scientist, Layer 6
Felipe Perez, Senior Machine Learning Research Scientist, Layer 6
WS2: Question Answering Systems in Responding to COVID-19 Open Research Dataset Challenge
In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19). In this workshop we demonstrate three Question Answering systems that were submitted to a Kaggle COVID-19 Open Research Dataset Challenge that could help the medical community develop answers to high priority scientific questions. The competition has launched to provide a chance for the machine learning research communities to employ advanced NLP methods to form a QA system for finding scientific answers for questions related to COVID-19. To do so, a dataset that is a collection of scholarly studies on the coronavirus group (i.e., referred to as the CORD-19) has been provided as a result of a collaboration between different research institutes such as Microsoft Research, the Allen Institute for AI, the National Library of Medicine at the NIH.
Facilitators: Rohan Bhambhoria, Graduate Researcher, Queen’s University Luna Feng, Research Scientist, Thomson Reuters
Hillary Ng, Graduate Researcher, Vector Institute, University of Toronto
Yoona Park, Graduate Researcher, Vector Institute, University of Toronto
Mah Parsa, Post-doc Researcher, Vector Institute, University of Toronto.
Event to conclude
Who should attend:
Individuals who are interested in learning more about natural language processing
Vector sponsors involved in the NLP project
Technical experts from Vector Sponsor companies
Vector PGA, Alumni, Scholarship recipient students interested in NLP