Tang, C., Lin, C. orcid.org/0000-0003-3454-2468, Huang, H. et al. (2 more authors) (2022) EtriCA: Event-triggered context-aware story generation augmented by cross attention. In: Goldberg, Y., Kozareva, Z. and Zhang, Y., (eds.) Findings of the Association for Computational Linguistics: EMNLP 2022. 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), 07-11 Dec 2022, Abu Dhabi. Association for Computational Linguistics , pp. 5504-5518.
Abstract
One of the key challenges of automatic story generation is how to generate a long narrative that can maintain fluency, relevance, and coherence. Despite recent progress, current story generation systems still face the challenge of how to effectively capture contextual and event features, which has a profound impact on a model's generation performance. To address these challenges, we present EtriCA, a novel neural generation model, which improves the relevance and coherence of the generated stories through residually mapping context features to event sequences with a cross-attention mechanism. Such a feature capturing mechanism allows our model to better exploit the logical relatedness between events when generating stories. Extensive experiments based on both automatic and human evaluations show that our model significantly outperforms state-of-the-art baselines, demonstrating the effectiveness of our model in leveraging context and event features.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2022 ACL. This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Nov 2022 15:15 |
Last Modified: | 03 Apr 2023 15:21 |
Published Version: | https://aclanthology.org/2022.findings-emnlp.403 |
Status: | Published |
Publisher: | Association for Computational Linguistics |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193591 |