Abstract: We propose a family of metrics to assess language generation derived from population estimation methods widely used in ecology. More specifically, we use mark-recapture and maximum-likelihood methods that have been applied over the past several decades to estimate the size of closed populations in the wild. We propose three novel metrics: ME<span class="tex-math"><span class="tex-math-unparsed">\text{Petersen}</span></span> and ME<span class="tex-math"><span class="tex-math-unparsed">\text{CAPTURE}</span></span>, which retrieve a single-valued assessment, and ME<span class="tex-math"><span class="tex-math-unparsed">\text{Schnabel}</span></span> which returns a double-valued metric to assess the evaluation set in terms of quality and diversity, separately. In synthetic experiments, our family of methods is sensitive to drops in quality and diversity. Moreover, our methods show a higher correlation to human evaluation than existing metrics on several challenging tasks, namely unconditional language generation, machine translation, and text summarization.