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Intro

Generative Models

Adversarial Training

Basic Paradigm

Problems with Generation • Over-emphasis of common outputs, fuzziness Adversarial

Training Method

In Equations

Problems w/ Training

Applications of GAN Objectives to Language

Problem! Can't Backprop through Sampling

Solution: Use Learning Methods for Latent Variables

Discriminators for Sequences

Stabilization Trick

Interesting Application: GAN for Data Cleaning (Yang et al. 2017)

Adversaries over Features vs. Over Outputs

Learning Domain-invariant Representations (Ganin et al. 2016) • Learn features that cannot be distinguished by domain

Adversarial Multi-task Learning (Liu et al. 2017)

Implicit Discourse Connection Classification w/ Adversarial Objective

Professor Forcing (Lamb et al. 2016)

Unsupervised Style Transfer for Text (Shen et al. 2017)

Description:

Explore adversarial learning in neural networks for natural language processing in this lecture from CMU's CS 11-747 course. Dive into generative adversarial networks (GANs), examining their application to both features and outputs in NLP tasks. Learn about the challenges of using GANs with discrete outputs and discover techniques for overcoming these obstacles. Investigate the use of adversarial training methods for domain adaptation, multi-task learning, and unsupervised style transfer in text. Gain insights into stabilization tricks and innovative applications like GAN-based data cleaning. Enhance your understanding of advanced NLP concepts through practical examples and theoretical explanations provided by Professor Graham Neubig.

Neural Nets for NLP 2017 - Adversarial Learning

Graham Neubig

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#Computer Science #Artificial Intelligence #Neural Networks #Natural Language Processing (NLP) #Data Science #Data Analysis #Data Cleaning