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Text generation is an important Natural Language Processing task with variousapplications. Although several metrics have already been introduced to evaluatethe text generation methods, each of them has its own shortcomings. The mostwidely used metrics such as BLEU only consider the quality of generatedsentences and neglect their diversity. For example, repeatedly generation ofonly one high quality sentence would result in a high BLEU score. On the otherhand, the more recent metric introduced to evaluate the diversity of generatedtexts known as Self-BLEU ignores the quality of generated texts. In this paper,we propose metrics to evaluate both the quality and diversity simultaneously byapproximating the distance of the learned generative model and the real datadistribution. For this purpose, we first introduce a metric that approximatesthis distance using n-gram based measures. Then, a feature-based measure whichis based on a recent highly deep model trained on a large text corpus calledBERT is introduced. Finally, for oracle training mode in which the generator'sdensity can also be calculated, we propose to use the distance measures betweenthe corresponding explicit distributions. Eventually, the most popular andrecent text generation models are evaluated using both the existing and theproposed metrics and the preferences of the proposed metrics are determined.

Jointly Measuring Diversity and Quality in Text Generation Models