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Neural Nets for NLP 2017 - Models of Dialog

Graham Neubig via YouTube

Overview

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This course on Models of Dialog for Neural Networks in Natural Language Processing covers chat-based dialog, task-based dialog, generation-based models, neural models for dialog response generation, dialog response retrieval, personality-infused dialog, dialog state tracking, and language generation from dialog state. The course aims to teach students how to build and evaluate various dialog systems using neural network models. The intended audience for this course is students or professionals interested in natural language processing, neural networks, and dialog systems. The teaching method includes lectures, slides, and references to related research papers for further exploration.

Syllabus

Intro
Types of Dialog
Two Paradigms
Generation-based Models (Ritter et al. 2011)
Neural Models for Dialog Response Generation
Hierarchical Encoder- decoder Model (Serban et al. 2016)
Dialog allows Much More Varied Responses
Diversity Promoting Objective for Conversation (Li et al. 2016)
Diversity is a Problem for Evaluation!
Using Multiple References with Human Evaluation Scores (Gallay et al. 2015)
Learning to Evaluate • Use context, true response, and actual response to learn a regressor that predicts goodness (Lowe et al. 2017) • Important similar to model, but has access to reference
Problem 3: Dialog Agents should have Personality
Personality Infused Dialog (Mairesse et al. 2007)
Persona-based Neural Dialog Model (Li et al. 2017)
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)
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

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