Unsupervised Text Generation - Techniques and Applications

Explore a novel search-and-learning framework for unsupervised text generation in this 24-minute conference talk by Dr. Lili Mou, Assistant Professor at the University of Alberta and Alberta Machine Intelligence Institute Fellow. Discover how heuristic scoring functions and stochastic local search techniques like simulated annealing can be used to generate high-quality candidate sentences for various natural language processing tasks. Learn about the integration of sequence-to-sequence models to improve inference efficiency and reduce search noise. Gain insights into the practical applications of this framework for industrial use, particularly for startups and new task development. Delve into Dr. Mou's extensive research background in deep learning applied to natural language processing and programming language processing, and understand how this innovative approach achieves high unsupervised performance across various natural language generation applications.

Unsupervised Text Generation Techniques and Applications