Natural Language Generation: Evaluation

This lecture covers the evaluation of natural language generation models, focusing on content overlap metrics, model-based metrics, and human evaluations. The instructor discusses the challenges of evaluating the quality of generated text, the limitations of content overlap metrics, and the importance of human judgments in assessing factuality and correctness. Various metrics such as BLEU, ROUGE, and BERTScore are explained, along with their applications in different NLP tasks. The lecture emphasizes the need for better evaluation methods and highlights the role of humans in assessing text generation systems.