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Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce.
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
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Best-Worst Scaling: A Model for the Largest Difference Judgments
Jordan J Louviere and George G Woodworth. 1991 · 1991
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Long Short-Term Memory
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Collective Content Selection for Concept-to-Text Generation
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Learning Semantic Correspondences with Less Supervision
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Sequence Transduction with Recurrent Neural Networks
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Inducing Document Plans for Concept-to-Text Generation
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Meteor Universal: Language Specific Translation Evaluation for Any Target Language
Michael J. Denkowski and Alon Lavie. 2014 · 2014
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Sequence to Sequence Learning with Neural Networks
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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
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Jordan J Louviere, Terry N Flynn, and Anthony Alfred John Marley. 2015 · 2015
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Effective Approaches to Attention-based Neural Machine Translation
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
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Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best-Worst Scaling
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
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Challenges in Data-to-document Generation
Sam Wiseman, Stuart M Shieber, and Alexander M Rush. 2017 · 2017
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Construction of the Literature Graph in Semantic Scholar
Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula, Iz Beltagy, Miles Crawford, Doug Downey, Jason Dunkelberger, Ahmed Elgohary, Sergey Feldman, Vu Ha, Rodney Kinney, Sebastian Kohlmeier, Kyle Lo, Tyler Murray, Hsu-Han Ooi, Matthew Peters, Joanna Power, Sam Skjonsberg, Lucy Lu Wang, Chris Wilhelm, Zheng Yuan, Madeleine van Zuylen, and Oren Etzioni. 2018 · 2018
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Graph-to-Sequence Learning using Gated Graph Neural Networks
Daniel Edward Robert Beck, Gholamreza Haffari, and Trevor Cohn. 2018 · 2018
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Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
Yi Luan, Luheng He, Mari Ostendorf, and Hannaneh Hajishirzi. 2018 · 2018
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Svetlana Kiritchenko and Saif Mohammad. 2016 · 2016
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Neural Text Generation from Structured Data with Application to the Biography Domain
Rémi Lebret, David Grangier, and Michael Auli. 2016 · 2016
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What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment
Hongyuan Mei, Mohit Bansal, and Matthew R Walter. 2016 · 2016
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Modeling Coverage for Neural Machine Translation
Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li. 2016 · 2016
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling. 2017 · 2017
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Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, and Luke S. Zettlemoyer. 2017 · 2017
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Get to the Point: Summarization with Pointer-Generator Networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
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Deep Graph Convolutional Encoders for Structured Data to Text Generation
Diego Marcheggiani and Laura Perez-Beltrachini. 2018 · 2018
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Improving Language Understanding by Generative Pre-Training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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A Graph-to-Sequence Model for AMR-to-Text Generation
Linfeng Song, Yue Zhang, Zhiguo Wang, and Daniel Gildea. 2018 · 2018
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Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
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Paper Abstract Writing through Editing Mechanism
Qingyun Wang, Zhihao Zhou, Lifu Huang, Spencer Whitehead, Boliang Zhang, Heng Ji, and Kevin Knight. 2018 · 2018
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Data-to-Text Generation with Content Selection and Planning
Ratish Puduppully, Li Dong, and Mirella Lapata. 2019 · 2019
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