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Evaluation practices in natural language generation (NLG) have many knownflaws, but improved evaluation approaches are rarely widely adopted. This issuehas become more urgent, since neural NLG models have improved to the pointwhere they can often no longer be distinguished based on the surface-levelfeatures that older metrics rely on. This paper surveys the issues with humanand automatic model evaluations and with commonly used datasets in NLG thathave been pointed out over the past 20 years. We summarize, categorize, anddiscuss how researchers have been addressing these issues and what theirfindings mean for the current state of model evaluations. Building on thoseinsights, we lay out a long-term vision for NLG evaluation and propose concretesteps for researchers to improve their evaluation processes. Finally, weanalyze 66 NLG papers from recent NLP conferences in how well they alreadyfollow these suggestions and identify which areas require more drastic changesto the status quo.

Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text