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Recent advances in large-scale language models (Raffel et al., 2019; Brown etal., 2020) have brought significant qualitative and quantitative improvementsin machine-driven text generation. Despite this, generation and evaluation ofmachine-generated narrative text remains a challenging problem. Objectiveevaluation of computationally-generated stories may be prohibitively expensive,require meticulously annotated datasets, or may not adequately measure thelogical coherence of a generated story's narratological structure.  Informed by recent advances in contrastive learning (Radford et al., 2021),we present Contrastive Authoring and Reviewing Pairing (CARP): a scalable,efficient method for performing qualitatively superior, zero-shot evaluation ofstories. We show a strong correlation between human evaluation of stories andthose of CARP. Model outputs more significantly correlate with correspondinghuman input than those language-model based methods which utilize finetuning orprompt engineering approaches. We also present and analyze the Story-CritiqueDataset, a new corpora composed of 1.3 million aligned story-critique pairsderived from over 80,000 stories. We expect this corpus to be of interest toNLP researchers.

Cut the CARP: Fishing for zero-shot story evaluation