Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts
Rujun Han, Hong Chen, Yufei Tian, and Nanyun Peng, in 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022.
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@inproceedings{han2022go, title = {Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts}, author = {Han, Rujun and Chen, Hong and Tian, Yufei and Peng, Nanyun}, booktitle = {2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2022} }
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Full Text BibTeX Details@inproceedings{yang2022re3, title = {Re3: Generating Longer Stories With Recursive Reprompting and Revision}, author = {Yang, Kevin and Tian, Yuandong and Peng, Nanyun and Klein, Dan}, booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year = {2022} }
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Full Text Code BibTeX Details@inproceedings{han2022go, title = {Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts}, author = {Han, Rujun and Chen, Hong and Tian, Yufei and Peng, Nanyun}, booktitle = {2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)}, year = {2022} }
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Full Text Slides Code Abstract BibTeX DetailsLong-form narrative text generated from largelanguage models manages a fluent impersonation of human writing, but only at the localsentence level, and lacks structure or global cohesion. We posit that many of the problem of story generation can be addressed via high quality content planning, and present a systemthat focuses on how to learn good plot structures to guide story generation. We utilize a plot-generation language model along with an ensemble of rescoring models that each implement an aspect of good story-writing as detailed in Aristotle’s Poetics. We find that stories written with our more principled plot structure are both more relevant to a given prompt and higher quality than baselines that do not content plan, or that plan in an unprincipled way.
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