This course covers models of dialog in the context of Neural Networks for NLP. The learning outcomes include understanding chat-based and task-based dialog, exploring generation-based models, and learning about dialog response generation. The course teaches skills such as using standard architecture with more context, implementing hierarchical encoder-decoder models, and discourse-level VAE models. The teaching method involves lectures and theoretical discussions. This course is intended for students and professionals interested in neural networks for natural language processing and dialog systems.
Overview
Syllabus
Intro
Types of Dialog
Two Paradigms
Generation-based Models (Ritter et al. 2011)
Neural Models for Dialog Response Generation
Dialog More Dependent on Global Coherence
One Solution: Use Standard Architecture w/ More Context
Hierarchical Encoder- decoder Model (Serban et al. 2016)
Discourse-level VAE Model (Zhao et al. 2017)
Diversity is a Problem for Evaluation!
Using Multiple References with Human Evaluation Scores (Galley et al. 2015)
Learning to Evaluate
Dialog Agents should have Personality
Personality Infused Dialog (Mairesse et al. 2007)
Dialog Response Retrieval
Retrieval-based Chat (Lee et al. 2009)
Neural Response Retrieval (Nio et al. 2014)
Smart Reply for Email Retrieval (Kannan et al. 2016)
Chat vs. Task Completion
Traditional Task-completion Dialog Framework
NLU (for Slot Filling) w/ Neural Nets (Mesnil et al. 2015)
Dialog State Tracking
Language Generation from Dialog State w/ Neural Nets (Wen et al. 2015)
Taught by
Graham Neubig
