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The goal of text generation models is to fit the underlying real probabilitydistribution of text. For performance evaluation, quality and diversity metricsare usually applied. However, it is still not clear to what extend can thequality-diversity evaluation reflect the distribution-fitting goal. In thispaper, we try to reveal such relation in a theoretical approach. We prove thatunder certain conditions, a linear combination of quality and diversityconstitutes a divergence metric between the generated distribution and the realdistribution. We also show that the commonly used BLEU/Self-BLEU metric pairfails to match any divergence metric, thus propose CR/NRR as a substitute forquality/diversity metric pair.

On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation