@inproceedings{lu-etal-2023-narrative,
title = "Narrative Order Aware Story Generation via Bidirectional Pretraining Model with Optimal Transport Reward",
author = "Lu, Zhicong and
Jin, Li and
Xu, Guangluan and
Hu, Linmei and
Liu, Nayu and
Li, Xiaoyu and
Sun, Xian and
Zhang, Zequn and
Wei, Kaiwen",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.415",
doi = "10.18653/v1/2023.findings-emnlp.415",
pages = "6274--6287",
abstract = "To create a captivating story, a writer often plans a sequence of logically coherent events and ingeniously manipulates the narrative order to generate flashback in place. However, existing storytelling systems suffer from both insufficient understanding of event correlations and inadequate awareness of event temporal order (e.g., go to hospital {\textless}after{\textgreater} get ill), making it challenging to generate high-quality events that balance the logic and narrative order of story. In this paper, we propose a narrative order aware framework BPOT (Bidirectional Pretraining Model with Optimal Transport Reward) for story generation, which presents a bidirectional pretrained model to encode event correlations and pairwise event order. We also design a reinforcement learning algorithm with novel optimal transport reward to further improve the quality of generated events in the fine-tuning stage. Specifically, a narrative order aware event sequence model is pretrained with the joint learning objectives of event blank infilling and pairwise order prediction. Then, reinforcement learning with novel optimal transport reward is designed to further improve the generated event quality in the fine-tuning stage. The novel optimal transport reward captures the mappings between the generated events and the sentences in the story, effectively measuring the quality of generated events. Both automatic and manual evaluation results demonstrate the superiority of our framework in generating logically coherent stories with flashbacks.",
}
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<abstract>To create a captivating story, a writer often plans a sequence of logically coherent events and ingeniously manipulates the narrative order to generate flashback in place. However, existing storytelling systems suffer from both insufficient understanding of event correlations and inadequate awareness of event temporal order (e.g., go to hospital \textlessafter\textgreater get ill), making it challenging to generate high-quality events that balance the logic and narrative order of story. In this paper, we propose a narrative order aware framework BPOT (Bidirectional Pretraining Model with Optimal Transport Reward) for story generation, which presents a bidirectional pretrained model to encode event correlations and pairwise event order. We also design a reinforcement learning algorithm with novel optimal transport reward to further improve the quality of generated events in the fine-tuning stage. Specifically, a narrative order aware event sequence model is pretrained with the joint learning objectives of event blank infilling and pairwise order prediction. Then, reinforcement learning with novel optimal transport reward is designed to further improve the generated event quality in the fine-tuning stage. The novel optimal transport reward captures the mappings between the generated events and the sentences in the story, effectively measuring the quality of generated events. Both automatic and manual evaluation results demonstrate the superiority of our framework in generating logically coherent stories with flashbacks.</abstract>
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%0 Conference Proceedings
%T Narrative Order Aware Story Generation via Bidirectional Pretraining Model with Optimal Transport Reward
%A Lu, Zhicong
%A Jin, Li
%A Xu, Guangluan
%A Hu, Linmei
%A Liu, Nayu
%A Li, Xiaoyu
%A Sun, Xian
%A Zhang, Zequn
%A Wei, Kaiwen
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F lu-etal-2023-narrative
%X To create a captivating story, a writer often plans a sequence of logically coherent events and ingeniously manipulates the narrative order to generate flashback in place. However, existing storytelling systems suffer from both insufficient understanding of event correlations and inadequate awareness of event temporal order (e.g., go to hospital \textlessafter\textgreater get ill), making it challenging to generate high-quality events that balance the logic and narrative order of story. In this paper, we propose a narrative order aware framework BPOT (Bidirectional Pretraining Model with Optimal Transport Reward) for story generation, which presents a bidirectional pretrained model to encode event correlations and pairwise event order. We also design a reinforcement learning algorithm with novel optimal transport reward to further improve the quality of generated events in the fine-tuning stage. Specifically, a narrative order aware event sequence model is pretrained with the joint learning objectives of event blank infilling and pairwise order prediction. Then, reinforcement learning with novel optimal transport reward is designed to further improve the generated event quality in the fine-tuning stage. The novel optimal transport reward captures the mappings between the generated events and the sentences in the story, effectively measuring the quality of generated events. Both automatic and manual evaluation results demonstrate the superiority of our framework in generating logically coherent stories with flashbacks.
%R 10.18653/v1/2023.findings-emnlp.415
%U https://aclanthology.org/2023.findings-emnlp.415
%U https://doi.org/10.18653/v1/2023.findings-emnlp.415
%P 6274-6287
Markdown (Informal)
[Narrative Order Aware Story Generation via Bidirectional Pretraining Model with Optimal Transport Reward](https://aclanthology.org/2023.findings-emnlp.415) (Lu et al., Findings 2023)
ACL
- Zhicong Lu, Li Jin, Guangluan Xu, Linmei Hu, Nayu Liu, Xiaoyu Li, Xian Sun, Zequn Zhang, and Kaiwen Wei. 2023. Narrative Order Aware Story Generation via Bidirectional Pretraining Model with Optimal Transport Reward. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6274–6287, Singapore. Association for Computational Linguistics.