Generating Natural-Language Text with Neural Networks

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Details

We are currently seeking a venue in the Domain area for this event. If your company would like to host, we will be happy to provide a pair of free tickets to the upcoming Texas AI Summit as a way of saying thanks. For details, send a note to [masked]

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Because it was held downtown, a lot of people couldn't make it to the encore presentation of Jonathan Mugan's Data Day Texas talk. So we are offering yet another encore presentation - this time in the Domain / 183 area.

Jonathan has revised and augmented this talk. Even if you already saw it at Data Day, expect some fresh content and insight this time.

This is a joint meetup with the Austin Natural Language Processing group.

Abstract

Automatic text generation enables computers to summarize text, to have conversations in customer-service and other settings, and to customize content based on the characteristics and goals of the human interlocutor. Using neural networks to automatically generate text is appealing because they can be trained through examples with no need to manually specify what should be said when. In this talk, we will provide an overview of the existing algorithms used in neural text generation, such as sequence2sequence models, reinforcement learning, variational methods, and generative adversarial networks. We will also discuss existing work that specifies how the content of generated text can be determined by manipulating a latent code. The talk will conclude with a discussion of current challenges and shortcomings of neural text generation.

About the speaker

Jonathan Mugan (http://www.jonathanmugan.com/) is a researcher specializing in artificial intelligence, machine learning, and natural language processing. His current research focuses in the area of deep learning for natural language generation and understanding. Dr. Mugan received his Ph.D. in Computer Science from the University of Texas at Austin. His thesis was centered in developmental robotics, which is an area of research that seeks to understand how robots can learn about the world in the same way that human children do. Dr. Mugan also held a post-doctoral position at Carnegie Mellon University, where he worked at the intersection of machine learning and human-computer interaction. One of the most requested speakers at the Data Day Texas (http://datadaytexas.com/) conferences, he recently also spoke on the topic of NLP at the O’Reilly AI (https://conferences.oreilly.com/artificial-intelligence/ai-ny-2017/public/schedule/speaker/273569) conference, and is the creator of the O’Reilly video course Natural Language Text Processing with Python (https://player.oreilly.com/videos/9781491976470). Dr. Mugan is also the author of The Curiosity Cycle: Preparing Your Child for the Ongoing Technological Explosion (https://www.amazon.com/Curiosity-Cycle-Second-Preparing-Technological/dp/0692022120).