Generating Natural-Language Text with Neural Networks

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We will be welcoming Dr. Jonathan Mugan (https://www.linkedin.com/in/jonathanmugan/) to speak to our group in conjunction with Austin Data Geeks on the evening of April 24th. He is an expert in natural language processing and artificial intelligence and I am very excited for him to come speak to us! We will plan on having a little networking time starting at 6:30 with snacks and drinks and will begin the talk shortly after 7pm. Please use the RSVP to let us know you will be there and we look forward to seeing you. There are several parking garages in the Domain near Accruent you can use for parking.

Generating Natural-Language Text with Neural Networks
Jonathan Mugan
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.

Jonathan Mugan (Austin) @jmugan
http://media.globaldatageeks.org/jonathan-mugan-200.jpgJonathan Mugan (Linkedin) 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 conferences, he recently also spoke on the topic of NLP at the O’Reilly AI conference, and is the creator of the O’Reilly video course Natural Language Text Processing with Python. Dr. Mugan is also the author of The Curiosity Cycle: Preparing Your Child for the Ongoing Technological Explosion.