Fetching the paper…
Reading the bibliography…
Story generation is a challenging task, which demands to maintain consistency of the plots and characters throughout the story.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. 2019 · 1906
Earlier work this paper cites.
Learning to predict explainable plots for neural story generation
Gang Chen, Yang Liu, Huanbo Luan, Meng Zhang, Qun Liu, and Maosong Sun. 2019 · 1912
Earlier work this paper cites.
Planning stories
Michael Lebowitz. 1987 · 1987
Earlier work this paper cites.
Mexica: A computer model of a cognitive account of creative writing
Rafael PÉrez Ý PÉrez and Mike Sharples. 2001 · 2001
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
The Penn discourse TreeBank 2.0
Rashmi Prasad, Nikhil Dinesh, Alan Lee, Eleni Miltsakaki, Livio Robaldo, Aravind Joshi, and Bonnie Webber. 2008 · 2008
Earlier work this paper cites.
Controlling narrative generation with planning trajectories: the role of constraints
Julie Porteous and Marc Cavazza. 2009 · 2009
Earlier work this paper cites.
Narrative planning: Balancing plot and character
Mark O Riedl and Robert Michael Young. 2010 · 2010
Earlier work this paper cites.
Automatic keyword extraction from individual documents
Stuart Rose, Dave Engel, Nick Cramer, and Wendy Cowley. 2010 · 2010
Earlier work this paper cites.
Story generation with crowdsourced plot graphs
Boyang Li, Stephen Lee-Urban, George Johnston, and Mark Riedl. 2013 · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Cited alongside, same era.
Variations of the similarity function of textrank for automated summarization
Federico Barrios, Federico López, Luis Argerich, and Rosa Wachenchauzer. 2016 · 2016
Cited alongside, same era.
Implicit discourse relation detection via a deep architecture with gated relevance network
Jifan Chen, Qi Zhang, Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2016 · 2016
Cited alongside, same era.
A persona-based neural conversation model
Jiwei Li, Michel Galley, Chris Brockett, Georgios Spithourakis, Jianfeng Gao, and Bill Dolan. 2016 · 2016
Cited alongside, same era.
Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
Later among the works it cites.
Event representations for automated story generation with deep neural nets
Lara J Martin, Prithviraj Ammanabrolu, Xinyu Wang, William Hancock, Shruti Singh, Brent Harrison, and Mark O Riedl. 2018 · 2018
Later among the works it cites.
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
Later among the works it cites.
Strategies for structuring story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2019 · 2019
Later among the works it cites.
DisSent: Learning sentence representations from explicit discourse relations
Allen Nie, Erin Bennett, and Noah Goodman. 2019 · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
Cited alongside, same era.
Story generation from sequence of independent short descriptions
Parag Jain, Priyanka Agrawal, Abhijit Mishra, Mohak Sukhwani, Anirban Laha, and Karthik Sankaranarayanan. 2017 · 2017
Cited alongside, same era.
Multi-task attention-based neural networks for implicit discourse relationship representation and identification
Man Lan, Jianxiang Wang, Yuanbin Wu, Zheng-Yu Niu, and Haifeng Wang. 2017 · 2017
Cited alongside, same era.
Deep enhanced representation for implicit discourse relation recognition
Hongxiao Bai and Hai Zhao. 2018 · 2018
Cited alongside, same era.
Neural text generation in stories using entity representations as context
Elizabeth Clark, Yangfeng Ji, and Noah A Smith. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
Do massively pretrained language models make better storytellers?
Abigail See, Aneesh Pappu, Rohun Saxena, Akhila Yerukola, and Christopher D. Manning. 2019 · 2019
Later among the works it cites.
Plan-and-write: Towards better automatic storytelling
Lili Yao, Nanyun Peng, Ralph Weischedel, Kevin Knight, Dongyan Zhao, and Rui Yan. 2019 · 2019
Later among the works it cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Closest in time.
A knowledge-enhanced pretraining model for commonsense story generation
Jian Guan, Fei Huang, Zhihao Zhao, Xiaoyan Zhu, and Minlie Huang. 2020 · 2020
Closest in time.