Natural Language Generation: Evaluating Text Quality

This lecture focuses on the evaluation of natural language generation (NLG) systems, discussing various metrics used to assess the quality of generated text. The instructor begins by outlining the key evaluation methods, including content overlap metrics, model-based metrics, and human evaluations. The lecture highlights the importance of perplexity as a measure of model quality, while also addressing its limitations in evaluating generated sentences. The discussion progresses to content overlap metrics, such as BLEU and ROUGE, which are commonly used but not ideal for open-ended tasks like dialogue and story generation. The instructor introduces semantic overlap metrics, including PYRAMID and SPICE, which provide a more nuanced evaluation of generated content. Model-based metrics are also explored, emphasizing the use of learned representations to assess semantic similarity. The lecture concludes with a discussion on the necessity of human evaluations, acknowledging their role as the gold standard despite being time-consuming and expensive. Overall, the lecture provides a comprehensive overview of the challenges and methodologies in evaluating NLG systems.