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NATURAL LANGUAGE PROCESSING (NLP) SYMPOSIUM

September 16 @ 10:00 am - 1:45 pm

 

Held Virtually – event open to Vector researchers and industry sponsors only

 

Register VIEW SEPT 15 AGENDA

 

September 16: AGENDA

Opening Remarks

10:00 am – 10:10 am

 

Sedef Akinli Kocak

MC: Sedef Akinli Kocak

Project Manager, Industry Innovation, Vector Institute

Cameron Schuler

Cameron Schuler

Chief Commercialization Officer and VP, Industry Innovation, Vector Institute

 

Keynote Presentation: AI at Work – How AI is transforming knowledge work

10:10 am – 10:40 am

In this talk, I will provide an overview of how AI in general and NLP in particular are transforming knowledge work and will share case studies from the Legal and News industries. I will also discuss our collaboration with the Vector Institute and other partner organizations on Deep Learning and the impact of deep learning on how Thomson Reuters develops AI-powered applications. I will conclude the talk with lessons learned from over 25 years of building machine learning and NLP applications and share my perspective on some of the opportunities that lie ahead.

 

Khalid Al-Kofahi

Khalid Al-Kofahi

Senior Vice President and Head of AI Personal Investments, Fidelity, former VP, Research and Development and Head of Center for AI and Cognitive Computing, Thomson Reuters

 

Keynote Presentation: Say ‘ah’: Speech and language in medicine

10:40 am – 11:00 am

The healthcare industry is rapidly acquiring data, but organizing and using those data remains a challenge. Speech and language data, in particular, have multiple peculiar aspects that need to be overcome, in order to really derive the potential benefit, and to make healthcare more efficient, more affordable, and less prone to error. I will present a few use cases of NLP in healthcare, and also highlight a few of the current hurdles to operating this exciting technology in practice

 

Frank Rudzicz

Frank Rudzicz

Associate 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

 

Project/Research Presentation

11:00 am – 11:30 am

Project Presentation: An Experimental Evaluation of Large NLP Models in the Biomedical Domain
With the growing amount of text in health data, there have been rapid advances in large pre-trained models that can be applied to a wide variety of biomedical tasks with minimal task-specific modifications. Emphasizing the cost of these models, which renders technical replication challenging, in this project we present experiments replicating BioBERT and further pre-training and fine-tuning in the biomedical domain. We also investigate the effectiveness of domain-specific and domain-agnostic pre-trained models across downstream biomedical NLP tasks. Our finding confirms that pre-trained models can be impactful in some downstream NLP tasks (QA and NER) in the biomedical domain.

 

Faiza Khattah Khan

Faiza Khattah Khan

Data Scientist, Manulife

 

Project Presentation: An Investigation of Transformer based model in Legal Texts
In this presentation we overview the results of some of the experiments we conducted to best customize the state-of-the-art contextualized transformer-based language models to address the specific characteristics of the legal domain texts.

 

Shohreh Shaghaghian

Shohreh Shaghaghian

Research Scientist at Center for AI and Cognitive Computing, Thomson Reuters

 

Student Presentation: Examining the rhetorical capacities of neural language models
Language models are key components for building neural NLP systems in discourse contexts. How should we choose between BERT, GPT-2, and other language models? We measure their abilities to understand rhetorical signals in texts, using a specially designed method called RST-probe.

 

Zining Zhu

Zining Zhu

PhD Student, University of Toronto

 

Panel Discussion: Business Impacts: What is the main risk to NLP or from NLP in the next 5 years?”

Moderator:

Frank Rudzicz

Frank Rudzicz

Associate 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

Stephany Lapierre

CEO, tealbook

Jimoh Ovbiagele

Jimoh Ovbiagele

Co-founder/CTO ROSS Intelligence

Yevgeniy Vahlis

Yevgeniy Vahlis

Head of Artificial Intelligence Capabilities, Bank of Montreal

Andrew Brown

Andrew Brown

Senior Director of Data Science and AI Research, CIBC

 

Networking and Poster Session

12 noon – 12:30 pm

 

Poster #1: Application of NLP in Emergency Medical Services

Amrit Sehdev

Queen’s University

Poster #2: Modelling Sentence Pairs via Reinforcement Learning: An Actor-Critic Approach to Learn the Irrelevant Words

Mahtab Ahmed

University of Western Ontario

Poster #3: SentenceMIM: A Latent Variable Language Model

Micha Livne

University of Toronto

Poster #4: Training without training data: Improving the generalizability of automated medical abbreviation disambiguation

Marta Skreta

University of Toronto

Poster #5: Explainability for deep learning text classifiers

Diana Lucaci

University of Ottawa

Poster #6: Identifying Clinical Terms in Medical Text Using Ontology-Guided Machine Learning

Aryan Arbabi

University of Toronto

Poster #7: Sharing is Caring: Exploring machine learning methods to facilitate medical imaging exchange using metadata only

Joanna Pineda

University of Toronto

Poster #8: GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference

Ali Hadi Zadeh

University of Toronto

Poster #9: Improved knowledge distillation by utilizing backward pass knowledge in neural networks

Aref Jafari

University of Waterloo

Poster #10: How Nouns Surface as Verbs: Inference and Generation in Word Class Conversion

Lei Yu

University of Toronto

Poster #11: Informal Natural Language Processing: The Case of Slang

Zhewei Sun

University of Toronto

Poster #12: Applications of the Chinese Remainder Theorem in Word Embedding Compression and Arithmetic

Patricia Thaine

University of Toronto

Poster #13: Predicting change in Major Depressive Disorder symptoms based on topic modelling features from psychiatric notes: An exploratory analysis

Marta Maslej

Centre for Addiction and Mental Health

Poster #14: Non-Pharmaceutical Intervention Discovery with Topic Modeling

Jonathan Smith

Layer 6

Poster #15: Hurtful words: quantifying biases in clinical contextual word embeddings

Haoran Zhang

University of Toronto

Poster #16: Domain Specific Fine-tuning of Denoising Sequence-to-Sequence Models for Natural Language Summarization

Matt Kalebic

PwC/Deloitte

Poster #17: An Experimental Evaluation of Large NLP Models in the Biomedical Domain

Faiza Khattak Khan

Data Scientist, Manulife

Poster #18: An Investigation of Transformer based model in Legal Texts

Shohreh Shaghaghian

Research Scientist at Center for AI and Cognitive Computing, Thomson Reuters

Poster #19: Examining the rhetorical capacities of neural language models

Zining Zhu

PhD Student, University of Toronto

Poster #20: Using natural language processing to predict splenomegaly from >100,000 structured CT reports

Karen Batch

Queen’s University

 

Networking and Break

12:30 pm – 12:45 pm

 

Concurrent Workshops

12:45 pm – 1:45 pm

 

WS1: How to use Fairseq (Facebook AI Research Sequence-to-Sequence Toolkit) for your own NLP application
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.

Level of workshop: Intermediate/Advanced

Joey Cheng

Joey Cheng

Machine Learning Research Scientist, Layer 6

Gary Huang

Gary Huang

Machine Learning Research Scientist, Layer 6

Felipe Perez

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.

Level of workshop: Intermediate

 Rohan Bhambhoria

Rohan Bhambhoria

PhD Researcher, Queen’s University

Luna Feng

Luna Feng

Research Scientist, Thomson Reuters

Hillary Ngai

Hillary Ngai

Graduate Researcher, Vector Institute, University of Toronto

Yoona Park

Yoona Park

Masters Student, Vector Institute, University of Toronto

Mah Parsa

Mah Parsa

Post-doc Researcher, Vector Institute, University of Toronto.

 

Event to conclude

1:45 pm

 

Register
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