Teaching Machines to Learn
I am a Master student in the Reasoning and Learning Lab at McGill University, Montreal, Canada. Supervised by Joelle Pineau, my research focuses on dialogue systems. I am especially interested in generative models as well as their automated evaluation. My favorite tools to study these questions are deep learning and reinforcement learning.
I'm also enthusiastic about other topics such as software development, mobile apps development, neuroscience, computational biology, computer security, and anything that is related to multimedia (3D animations, movie making, music).
I obtained a bachelor of science (B.Sc.) in Computer Science from McGill University in 2016. In between my semesters I did multiple internships. Most notably I worked as a Software Developer Intern from January to August 2016 at Nuance Communications Inc.
For more details, please find my CV here.
- NIPS Conversational Intelligence Challenge [link]; Nicolas Angleard-Gontier, Koustuv Sinha, Prasanna Parthasarathi, Michael Noseworthy, and Peter Henderson; 05/2017 - ongoing; [round1 overview]
- Ubuntu Dialogue Corpus with HRED and attention ; Nuance Communications Summer Internship ; 05-09/2017; [report] [poster]
- "Training a Discriminator to Compare Generative Dialogue Models"; Nicolas Angelard-Gontier; 2017; [report] [slides] [code]
- "Policy Gradient Methods for Dialogue Response Generation"; Michael Noseworthy, Nicolas Angelard-Gontier; 2017; [report] [slides] [code -private for now]
- "Sentiment Analysis on Movie Reviews"; Nicolas Angelard-Gontier; 2016; [report] [slides] [code]
Previous projects (not related to Machine Learning) can be found in the archive page.
- "Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses"; Ryan Lowe, Michael Noseworthy, Iulian Serban, Nicolas Angelard-Gontier, Yoshua Bengio, Joelle Pineau; ICLR 2017; [paper]