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The neural attention model has achieved great success in data-to-text generation tasks.
Improving multi-turn dialogue modelling with utterance rewriter
Hui Su, Xiaoyu Shen, Rongzhi Zhang, Fei Sun, Pengwei Hu, Cheng Niu, and Jie Zhou. 2019 · 1906
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Select and attend: Towards controllable content selection in text generation
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Design of a knowledge-based report generator
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A tutorial on hidden markov models and selected applications in speech recognition
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The mathematics of statistical machine translation: Parameter estimation
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Improved alignment models for statistical machine translation
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Parameter estimation for probabilistic finite-state transducers
Jason Eisner. 2002 · 2002
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Unsupervised pidgin text generation by pivoting english data and self-training
Ernie Chang, David Ifeoluwa Adelani, Xiaoyu Shen, and Vera Demberg. 2020 · 2003
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie. 2005 · 2005
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Collective content selection for concept-to-text generation
Regina Barzilay and Mirella Lapata. 2005 · 2005
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Induction of word and phrase alignments for automatic document summarization
Hal Daumé III and Daniel Marcu. 2005 · 2005
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Comparing automatic and human evaluation of nlg systems
Anja Belz and Ehud Reiter. 2006 · 2006
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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 semantic correspondences with less supervision
Percy Liang, Michael I Jordan, and Dan Klein. 2009 · 2009
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An investigation into the validity of some metrics for automatically evaluating natural language generation systems
Ehud Reiter and Anja Belz. 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
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A statistical nlg framework for aggregated planning and realization
Ravi Kondadadi, Blake Howald, and Frank Schilder. 2013 · 2013
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A global model for concept-to-text generation
Ioannis Konstas and Mirella Lapata. 2013 · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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Multi-resolution language grounding with weak supervision
Rik Koncel-Kedziorski, Hannaneh Hajishirzi, and Ali Farhadi. 2014 · 2014
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A template-based abstractive meeting summarization: Leveraging summary and source text relationships
Tatsuro Oya, Yashar Mehdad, Giuseppe Carenini, and Raymond Ng. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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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Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015
Towards neural phrase-based machine translation
Po-Sen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, and Li Deng. 2018 · 2018
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A deep ensemble model with slot alignment for sequence-to-sequence natural language generation
Juraj Juraska, Panagiotis Karagiannis, Kevin Bowden, and Marilyn Walker. 2018 · 2018
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A tutorial on deep latent variable models of natural language
Yoon Kim, Sam Wiseman, and Alexander M Rush. 2018 · 2018
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Structurebased generation system for e2e nlg challenge
Dang Tuan Nguyen and Trung Tran. 2018 · 2018
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E2e nlg challenge: Neural models vs. templates
Yevgeniy Puzikov and Iryna Gurevych. 2018 · 2018
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Learning latent semantic annotations for grounding natural language to structured data
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The WebNLG challenge: Generating text from DBPedia data
Emilie Colin, Claire Gardent, Yassine M’rabet, Shashi Narayan, and Laura Perez-Beltrachini. 2016 · 2016
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Inside-outside and forward-backward algorithms are just backprop (tutorial paper)
Jason Eisner. 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, TTI UChicago, Mohit Bansal, and Matthew R Walter. 2016 · 2016
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Online segment to segment neural transduction
Lei Yu, Jan Buys, and Phil Blunsom. 2016 · 2016
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Image-to-markup generation with coarse-to-fine attention
Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, and Alexander M Rush. 2017 · 2017
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Guanghui Qin, Jin-Ge Yao, Xuening Wang, Jinpeng Wang, and Chin-Yew Lin. 2018 · 2018
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Can neural generators for dialogue learn sentence planning and discourse structuring?
Lena Reed, Shereen Oraby, and Marilyn Walker. 2018 · 2018
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Nexus network: Connecting the preceding and the following in dialogue generation
Hui Su, Xiaoyu Shen, Wenjie Li, and Dietrich Klakow. 2018 · 2018
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Neural hidden Markov model for machine translation
Weiyue Wang, Derui Zhu, Tamer Alkhouli, Zixuan Gan, and Hermann Ney. 2018 · 2018
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Learning neural templates for text generation
Sam Wiseman, Stuart M Shieber, and Alexander M Rush. 2018 · 2018
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Hard non-monotonic attention for character-level transduction
Shijie Wu, Pamela Shapiro, and Ryan Cotterell. 2018 · 2018
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A comprehensive study: Sentence compression with linguistic knowledge-enhanced gated neural network
Yang Zhao, Xiaoyu Shen, Hajime Senuma, and Akiko Aizawa. 2018 · 2018
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Constrained decoding for neural NLG from compositional representations in task-oriented dialogue
Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, and Rajen Subba. 2019 · 2019
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Neural data-to-text generation: A comparison between pipeline and end-to-end architectures
Thiago Castro Ferreira, Chris van der Lee, Emiel van Miltenburg, and Emiel Krahmer. 2019 · 2019
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Unsupervised word discovery with segmental neural language models
Kazuya Kawakami, Chris Dyer, and Phil Blunsom. 2019 · 2019
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Step-by-step: Separating planning from realization in neural data-to-text generation
Amit Moryossef, Yoav Goldberg, and Ido Dagan. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
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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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Posterior attention models for sequence to sequence learning
Shiv Shankar and Sunita Sarawagi. 2019 · 2019
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Improving latent alignment in text summarization by generalizing the pointer generator
Xiaoyu Shen, Yang Zhao, Hui Su, and Dietrich Klakow. 2019b · 2019
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Low-resource response generation with template prior
Ze Yang, wei wu, Jian Yang, Can Xu, and zhoujun li. 2019 · 2019
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Evaluating the state-of-the-art of end-to-end natural language generation: The e2e nlg challenge
Ondřej Dušek, Jekaterina Novikova, and Verena Rieser. 2020 · 2020
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Posterior regularization for structured latent variable models
Kuzman Ganchev, Jennifer Gillenwater, Ben Taskar, et al. 2010 · 2049
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