Measuring event salience is essential in the understanding of stories. This paper takes a recent unsupervised method for salience detection derived from Barthes Cardinal Functions and theories of surprise and applies it to longer narrative forms. We improve the standard transformer language model by incorporating an external knowledgebase (derived from Retrieval Augmented Generation) and adding a memory mechanism to enhance performance on longer works. We use a novel approach to derive salience annotation using chapter-aligned summaries from the Shmoop corpus for classic literary works. Our evaluation against this data demonstrates that our salience detection model improves performance over and above a non-knowledgebase and memory augmented language model, both of which are crucial to this improvement.

Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories


<i>**Session Chair: **Chrysoula (Chryssa) Zerva</i><br><br>
<i>**In Person Volunteer:** Amit Kumar Sah</i><br>
<i>** Remote Volunteer:** WenChang Ma</i>

2D: NLP Applications 1

technical paper

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EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

David Wilmot

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Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories

Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories

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