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Previous work has demonstrated the effectiveness of planning for story generation exclusively in a monolingual setting focusing primarily on English.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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An interactive program that writes stories
James Meehan. 1977 · 1977
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Dialogue Games: An Approach to Discourse Analysis
L Carlson. 1983 · 1983
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Planning stories
Michael Lebowitz. 1987 · 1987
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Ministrel: A Computer Model of Creativity and Sotrytelling
Scott R. Turner. 1992 · 1992
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Centering: A framework for modeling the local coherence of discourse
Barbara Grosz, Aravind K. Joshi, and Scott Weinstein. 1995 · 1995
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Story: Substance, Structure, Style, and the Principles of Screenwriting
Robert McKee. 1997 · 1997
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Mexica: A computer model of a cognitive account of creative writing
Rafael Pérez y Pérez and Mike Sharples. 2001 · 2001
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Using commonsense reasoning to generate stories
Hugo Liu and Push Singh. 2002 · 2002
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Interactingwith virtual agents in mixed reality interactive storytelling
Marc Cavazza, Olivier Martin, Fred Charles, Steven J Mead, and Xavier Marichal. 2003 · 2003
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Screenplay: The foundations of screenwriting
Syd Field. 2005 · 2005
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Creativity issues in plot generation
Federico Peinado and Pablo Gervás. 2005 · 2005
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Natural language processing with Python: analyzing text with the natural language toolkit
Steven Bird, Ewan Klein, and Edward Loper. 2009 · 2009
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Narrative planning: Balancing plot and characte
Mark O Riedl and Robert Michael Young. 2010 · 2010
Earlier work this paper cites.
Modeling narrative conflict to generate interesting stories
Stephen Ware and robert Michael Young. 2010 · 2010
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Information structure in discourse: Towards an integrated formal theory of pragmat ics
Craige Roberts. 2012 · 2012
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The Irresistible Fairy Tale: The Cultural and Social History of a Genre
Jack Zipes. 2012 · 2012
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How to Write Dazzling Dialogue: The Fastest Way to Improve Any Manuscript
James Scott Bell. 2014 · 2014
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A corpus and cloze evaluation for deeper understanding of commonsense stories
Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016 · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016 · 2016
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Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
Cited alongside, same era.
SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
Taku Kudo and John Richardson. 2018 · 2018
Cited alongside, same era.
Towards controllable story generation
Nanyun Peng, Marjan Ghazvininejad, Jonathan May, and Kevin Knight. 2018 · 2018
Cited alongside, same era.
A skeleton-based model for promoting coherence among sentences in narrative story generation
Jingjing Xu, Xuancheng Ren, Yi Zhang, Qi Zeng, Xiaoyan Cai, and Xu Sun. 2018 · 2018
Cited alongside, same era.
Strategies for structuring story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2019 · 2019
Cited alongside, same era.
A survey of deep learning applied to story generation
Chenglong Hou, Chensong Zhou, Kun Zhou, Jinan Sun, and Sisi Xuanyuan. 2019 · 2019
Controllable neural dialogue summarization with personal named entity planning
Zhengyuan Liu and Nancy Chen. 2021 · 2021
Later among the works it cites.
Planning with learned entity prompts for abstractive summarization
Shashi Narayan, Yao Zhao, Joshua Maynez, Gonçalo Simões, Vitaly Nikolaev, and Ryan McDonald. 2021 · 2021
Later among the works it cites.
Mauve: Measuring the gap between neural text and human text using divergence frontiers
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. 2021 · 2021
Later among the works it cites.
Data-to-text generation with macro planning
Ratish Puduppully and Mirella Lapata. 2021 · 2021
Later among the works it cites.
Prompt programming for large language models: Beyond the few-shot paradigm
Laria Reynolds and Kyle McDonell. 2021 · 2021
Later among the works it cites.
It’s not just size that matters: Small language models are also few-shot learners
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Cited alongside, same era.
Unsupervised hierarchical story infilling
Daphne Ippolito, David Grangier, Chris Callison-Burch, and Douglas Eck. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Cited alongside, same era.
T-CVAE: Transformer-based conditioned variational autoencoder for story completion
Tianming Wang and Xiaojun Wan. 2019 · 2019
Cited alongside, same era.
Plan-and-write: Towards better automatic storytelling
Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, and Rui Yan. 2019 · 2019
Cited alongside, same era.
Content planning for neural story generation with aristotelian rescoring
Seraphina Goldfarb-Tarrant, Tuhin Chakrabarty, Ralph Weischedel, and Nanyun Peng. 2020 · 2020
Cited alongside, same era.
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
Timo Schick and Hinrich Schütze. 2021 · 2021
Later among the works it cites.
Language models are few-shot multilingual learners
Genta Indra Winata, Andrea Madotto, Zhaojiang Lin, Rosanne Liu, Jason Yosinski, and Pascale Fung. 2021 · 2021
Later among the works it cites.
mt5: A massively multilingual pre-trained text-to-text transformer
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021a · 2021
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mT5: A massively multilingual pre-trained text-to-text transformer
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021b · 2021
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Qameleon: Multilingual qa with only 5 examples
Priyanka Agrawal, Chris Alberti, Fantine Huot, Joshua Maynez, Ji Ma, Sebastian Ruder, Kuzman Ganchev, Dipanjan Das, and Mirella Lapata. 2022 · 2022
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Ask me anything: A simple strategy for prompting language models
Simran Arora, Avanika Narayan, Mayee F Chen, Laurel J Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, and Christopher Ré. 2022 · 2022
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Of human criteria and automatic metrics: A benchmark of the evaluation of story generation
Cyril Chhun, Pierre Colombo, Fabian M. Suchanek, and Chloé Clavel. 2022 · 2022
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022
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Polyglot prompt: Multilingual multitask promptraining
Jinlan Fu, See-Kiong Ng, and Pengfei Liu. 2022 · 2022
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Language models are general-purpose interfaces
Yaru Hao, Haoyu Song, Li Dong, Shaohan Huang, Zewen Chi, Wenhui Wang, Shuming Ma, and Furu Wei. 2022 · 2022
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Few-shot learning with multilingual language models
Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona T. Diab, Veselin Stoyanov, and Xian Li. 2021 · 2022
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Plug-and-play controller for story completion: A pilot study toward emotion-aware story writing assistance
Yusuke Mori, Hiroaki Yamane, Ryohei Shimizu, and Tatsuya Harada. 2022 · 2022
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Context-tuning: Learning contextualized prompts for natural language generation
Tianyi Tang, Junyi Li, Wayne Xin Zhao, and Ji-Rong Wen. 2022 · 2022
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Overcoming catastrophic forgetting in zero-shot cross-lingual generation
Tu Vu, Aditya Barua, Brian Lester, Daniel Cer, Mohit Iyyer, and Noah Constant. 2022 · 2022
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Re3: Generating longer stories with recursive reprompting and revision
Kevin Yang, Nanyun Peng, Yuandong Tian, and Dan Klein. 2022 · 2022
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