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        <title>Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories</title>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.65</identifier>
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%0 Conference Proceedings
%T Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories
%A Wilmot, David
%A Keller, Frank
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F wilmot-keller-2021-memory
%X 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.
%R 10.18653/v1/2021.emnlp-main.65
%U https://aclanthology.org/2021.emnlp-main.65
%U https://doi.org/10.18653/v1/2021.emnlp-main.65
%P 851-865

Markdown (Informal)

[Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories](https://aclanthology.org/2021.emnlp-main.65) (Wilmot & Keller, EMNLP 2021)

• Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories (Wilmot & Keller, EMNLP 2021)

ACL

• David Wilmot and Frank Keller. 2021. Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 851–865, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.