Natural Language Generation

This lecture covers the fundamentals of Natural Language Generation (NLG), including neural NLG models, decoding strategies, training algorithms, and evaluation methods. It delves into the challenges of exposure bias, biases in text generation models, and ethical considerations. The lecture emphasizes the importance of human evaluations and the need for safeguards to prevent harmful content generation. It also explores advanced topics such as model-based metrics, hidden biases, and the impact of large-scale language models on NLG research.