Explores the evaluation of natural language generation models, emphasizing the importance of human judgments and the limitations of content overlap metrics.
Explores natural language generation, focusing on building systems that produce coherent text for human consumption using various decoding methods and evaluation metrics.
Covers Variational Autoencoders, a probabilistic approach to autoencoders for data generation and feature representation, with applications in Natural Language Processing.
