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With the recent advances of open-domain story generation, the lack ofreliable automatic evaluation metrics becomes an increasingly imperative issuethat hinders the fast development of story generation. According to conductedresearches in this regard, learnable evaluation metrics have promised moreaccurate assessments by having higher correlations with human judgments. Acritical bottleneck of obtaining a reliable learnable evaluation metric is thelack of high-quality training data for classifiers to efficiently distinguishplausible and implausible machine-generated stories. Previous works relied on\textit{heuristically manipulated} plausible examples to mimic possible systemdrawbacks such as repetition, contradiction, or irrelevant content in the textlevel, which can be \textit{unnatural} and \textit{oversimplify} thecharacteristics of implausible machine-generated stories. We propose to tacklethese issues by generating a more comprehensive set of implausible storiesusing {\em plots}, which are structured representations of controllable factorsused to generate stories. Since these plots are compact and structured, it iseasier to manipulate them to generate text with targeted undesirableproperties, while at the same time maintain the grammatical correctness andnaturalness of the generated sentences. To improve the quality of generatedimplausible stories, we further apply the adversarial filtering procedurepresented by \citet{zellers2018swag} to select a more nuanced set ofimplausible texts. Experiments show that the evaluation metrics trained on ourgenerated data result in more reliable automatic assessments that correlateremarkably better with human judgments compared to the baselines.

Plot-guided Adversarial Example Construction for Evaluating Open-domain Story Generation