@inproceedings{tang-etal-2022-etrica,
title = "{E}tri{CA}: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention",
author = "Tang, Chen and
Lin, Chenghua and
Huang, Henglin and
Guerin, Frank and
Zhang, Zhihao",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.403",
doi = "10.18653/v1/2022.findings-emnlp.403",
pages = "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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention
%A Tang, Chen
%A Lin, Chenghua
%A Huang, Henglin
%A Guerin, Frank
%A Zhang, Zhihao
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F tang-etal-2022-etrica
%X 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.
%R 10.18653/v1/2022.findings-emnlp.403
%U https://aclanthology.org/2022.findings-emnlp.403
%U https://doi.org/10.18653/v1/2022.findings-emnlp.403
%P 5504-5518
Markdown (Informal)
[EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention](https://aclanthology.org/2022.findings-emnlp.403) (Tang et al., Findings 2022)
ACL