Fetching the paper…
Reading the bibliography…
Story generation, namely generating a reasonable story from a leading context, is an important but challenging task.
Transfertransfo: A transfer learning approach for neural network based conversational agents
Thomas Wolf, Victor Sanh, Julien Chaumond, and Clement Delangue. 2019 · 1901
Earlier work this paper cites.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Maxwell Forbes, and Yejin Choi. 2019 · 1904
Earlier work this paper cites.
Ernie: Enhanced representation through knowledge integration
Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, and Hua Wu. 2019 · 1904
Earlier work this paper cites.
Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 1905
Earlier work this paper cites.
Measuring nominal scale agreement among many raters
Joseph L Fleiss. 1971 · 1971
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.
Automatic keyword extraction from individual documents
Stuart Rose, Dave Engel, Nick Cramer, and Wendy Cowley. 2010 · 2010
Earlier work this paper cites.
Types of common-sense knowledge needed for recognizing textual entailment
Peter LoBue and Alexander Yates. 2011 · 2011
Earlier work this paper cites.
Representing general relational knowledge in conceptnet 5
R Speer and C Havasi. 2012 · 2012
Earlier work this paper cites.
Enhancing topic-to-essay generation with external commonsense knowledge
Pengcheng Yang, Lei Li, Fuli Luo, Tianyu Liu, and Xu Sun. 2019b · 2012
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
Earlier work this paper cites.
Distributed representations of sentences and documents
Quoc Le and Tomas Mikolov. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning. 2015 · 2015
Earlier work this paper cites.
Skip-thought vectors
Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Earlier work this paper cites.
Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016 · 2016
Earlier work this paper cites.
Visual storytelling
Ting-Hao Kenneth Huang, Francis Ferraro, Nasrin Mostafazadeh, Ishan Misra, Aishwarya Agrawal, Jacob Devlin, Ross Girshick, Xiaodong He, Pushmeet Kohli, Dhruv Batra, et al. 2016 · 2016
Cited alongside, same era.
A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016a · 2016
Cited alongside, same era.
Writing Stories with Help from Recurrent Neural Networks
Melissa Roemmele. 2016 · 2016
Cited alongside, same era.
Chinese poetry generation with planning based neural network
Zhe Wang, Wei He, Hua Wu, Haiyang Wu, Wei Li, Haifeng Wang, and Enhong Chen. 2016 · 2016
Cited alongside, same era.
Learning sentence embeddings with auxiliary tasks for cross-domain sentiment classification
Jianfei Yu and Jing Jiang. 2016 · 2016
Cited alongside, same era.
Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. 2017 · 2017
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 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.
Commonsense knowledge aware conversation generation with graph attention
Hao Zhou, Tom Yang, Minlie Huang, Haizhou Zhao, Jingfang Xu, and Xiaoyan Zhu. 2018 · 2018
Later among the works it cites.
Fine-tuning pre-trained transformer language models to distantly supervised relation extraction
Christoph Alt, Marc Hübner, and Leonhard Hennig. 2019 · 2019
Later among the works it cites.
COMET: Commonsense transformers for automatic knowledge graph construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Dynamic entity representations in neural language models
Yangfeng Ji, Chenhao Tan, Sebastian Martschat, Yejin Choi, and Noah A Smith. 2017 · 2017
Cited alongside, same era.
Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Learning discourse-level diversity for neural dialog models using conditional variational autoencoders
Tiancheng Zhao, Ran Zhao, and Maxine Eskenazi. 2017 · 2017
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.
Strategies for structuring story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2019 · 2019
Later among the works it cites.
Story ending generation with incremental encoding and commonsense knowledge
Jian Guan, Yansen Wang, and Minlie Huang. 2019 · 2019
Later among the works it cites.
Pretraining methods for dialog context representation learning
Shikib Mehri, Evgeniia Razumovskaia, Tiancheng Zhao, and Maxine Eskenazi. 2019 · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
Atomic: an atlas of machine commonsense for if-then reasoning
Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A Smith, and Yejin Choi. 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.
Long and diverse text generation with planning-based hierarchical variational model
Zhihong Shao, Minlie Huang, Jiangtao Wen, Wenfei Xu, and Xiaoyan Zhu. 2019 · 2019
Later among the works it cites.
Mass: Masked sequence to sequence pre-training for language generation
Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. 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.
ERNIE: Enhanced language representation with informative entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019 · 2019
Later among the works it cites.
Story ending selection by finding hints from pairwise candidate endings
Mantong Zhou, Minlie Huang, and Xiaoyan Zhu. 2019 · 2019
Later among the works it cites.