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We propose the Detailed Outline Control (DOC) framework for improving long-range plot coherence when automatically generating several-thousand-word-long stories.
Language models are few-shot learners
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Roberta: A robustly optimized bert pretraining approach
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 1908
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Counterfactual story reasoning and generation
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Plug and play language models: A simple approach to controlled text generation
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Plotmachines: Outline-conditioned generation with dynamic plot state tracking
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Content planning for neural story generation with aristotelian rescoring
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Gedi: Generative discriminator guided sequence generation
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, Nitish Shirish Keskar, Shafiq Joty, Richard Socher, and Nazneen Fatema Rajani. 2020 · 2009
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Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Raul Puri, Pascale Fung, Anima Anandkumar, and Bryan Catanzaro. 2020 · 2010
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Hierarchical attention networks for document classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016 · 2016
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Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
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Document-level neural machine translation with hierarchical attention networks
Lesly Miculicich, Dhananjay Ram, Nikolaos Pappas, and James Henderson. 2018 · 2018
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Improved lexically constrained decoding for translation and monolingual rewriting
J Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, and Benjamin Van Durme. 2019 · 2019
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T-cvae: Transformer-based conditioned variational autoencoder for story completion
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Less annotating, more classifying–addressing the data scarcity issue of supervised machine learning with deep transfer learning and bert-nli
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Multitask prompted training enables zero-shot task generalization
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