Enhanced Story Representation by ConceptNet for Predicting Story Endings

Citations Over TimeTop 19% of 2020 papers

Abstract

Predicting endings for narrative stories is a grand challenge for machine commonsense reasoning. The task requires ac- curate representation of the story semantics and structured logic knowledge. Pre-trained language models, such as BERT, made progress recently in this task by exploiting spurious statistical patterns in the test dataset, instead of 'understanding' the stories per se. In this paper, we propose to improve the representation of stories by first simplifying the sentences to some key concepts and second modeling the latent relation- ship between the key ideas within the story. Such enhanced sentence representation, when used with pre-trained language models, makes substantial gains in prediction accuracy on the popular Story Cloze Test without utilizing the biased validation data.

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