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We conduct a large-scale, systematic study to evaluate the existingevaluation methods for natural language generation in the context of generatingonline product reviews. We compare human-based evaluators with a variety ofautomated evaluation procedures, including discriminative evaluators thatmeasure how well machine-generated text can be distinguished from human-writtentext, as well as word overlap metrics that assess how similar the generatedtext compares to human-written references. We determine to what extent thesedifferent evaluators agree on the ranking of a dozen of state-of-the-artgenerators for online product reviews. We find that human evaluators do notcorrelate well with discriminative evaluators, leaving a bigger question ofwhether adversarial accuracy is the correct objective for natural languagegeneration. In general, distinguishing machine-generated text is challengingeven for human evaluators, and human decisions correlate better with lexicaloverlaps. We find lexical diversity an intriguing metric that is indicative ofthe assessments of different evaluators. A post-experiment survey ofparticipants provides insights into how to evaluate and improve the quality ofnatural language generation systems.

Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation