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Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records.
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Towards broad coverage surface realization with ccg
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Generation by inverting a semantic parser that uses statistical machine translation
Yuk Wah Wong and Raymond J Mooney. 2007 · 2007
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Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models
Anja Belz. 2008 · 2008
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Learning to sportscast: a test of grounded language acquisition
David L Chen and Raymond J Mooney. 2008 · 2008
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Learning semantic correspondences with less supervision
Percy Liang, Michael I Jordan, and Dan Klein. 2009 · 2009
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A simple domain-independent probabilistic approach to generation
Gabor Angeli, Percy Liang, and Dan Klein. 2010 · 2010
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Generative alignment and semantic parsing for learning from ambiguous supervision
Joohyun Kim and Raymond J Mooney. 2010 · 2010
Classifying relations by ranking with convolutional neural networks
Cícero Nogueira dos Santos, Bing Xiang, and Bowen Zhou. 2015 · 2015
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Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O. K. Li. 2016 · 2016
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Pointing the unknown words
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Data recombination for neural semantic parsing
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Adversarial evaluation of dialogue models
Anjuli Kannan and Oriol Vinyals. 2016 · 2016
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Recurrent neural network based language model
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Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel P. Kuksa. 2011 · 2011
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A probabilistic forest-to-string model for language generation from typed lambda calculus expressions
Wei Lu and Hwee Tou Ng. 2011 · 2011
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Generating text with recurrent neural networks
Ilya Sutskever, James Martens, and Geoffrey E Hinton. 2011 · 2011
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A global model for concept-to-text generation
Ioannis Konstas and Mirella Lapata. 2013 · 2013
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On the properties of neural machine translation: Encoder-decoder approaches
KyungHyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014 · 2014
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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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How NOT to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation
Chia-Wei Liu, Ryan Lowe, Iulian Serban, Michael Noseworthy, Laurent Charlin, and Joelle Pineau. 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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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 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, et al. 2016 · 2016
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Reference-aware language models
Zichao Yang, Phil Blunsom, Chris Dyer, and Wang Ling. 2016 · 2016
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Learning to generate one-sentence biographies from wikidata
Andrew Chisholm, Will Radford, and Ben Hachey. 2017 · 2017
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Opennmt: Open-source toolkit for neural machine translation
Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M. Rush. 2017 · 2017
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Adversarial learning for neural dialogue generation
Jiwei Li, Will Monroe, Tianlin Shi, Alan Ritter, and Dan Jurafsky. 2017 · 2017
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You need to understand your corpora! the weathergov example
Ehud Reiter. 2017 · 2017
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Neural machine translation with reconstruction
Zhaopeng Tu, Yang Liu, Lifeng Shang, Xiaohua Liu, and Hang Li. 2017 · 2017
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Semi-supervised learning for relation extraction
GuoDong Zhou, JunHui Li, LongHua Qian, and Qiaoming Zhu. 2008 · 2017
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