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To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison.
Avoiding a tragedy of the commons in the peer review process
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Generating long and informative reviews with aspect-aware coarse-to-fine decoding
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PaperRobot: Incremental draft generation of scientific ideas
Qingyun Wang, Lifu Huang, Zhiying Jiang, Kevin Knight, Heng Ji, Mohit Bansal, and Yi Luan. 2019 · 1991
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Kelvin Luu, Rik Koncel-Kedziorski, Kyle Lo, Isabel Cachola, and Noah A. Smith. 2020 · 2002
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A maximum entropy approach to HowNet-based Chinese word sense disambiguation
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Chinese word sense disambiguation with PageRank and HowNet
Jinghua Wang, Jianyi Liu, and Ping Zhang. 2008 · 2008
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Conference reviewing considered harmful
Thomas Anderson. 2009 · 2009
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A reliability-generalization study of journal peer reviews: A multilevel meta-analysis of inter-rater reliability and its determinants
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Generating fine-grained reviews of songs from album reviews
Swati Tata and Barbara Di Eugenio. 2010 · 2010
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Improving our reviewing processes
Inderjeet Mani. 2011 · 2011
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Unsupervised approximate-semantic vocabulary learning for human action and video classification
Qiong Zhao and Horace HS Ip. 2013 · 2013
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Learning phrase representations using RNN encoder–decoder for statistical machine translation
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Jure Leskovec and Wei Wang. 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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Exploiting knowledge base to generate responses for natural language dialog listening agents
Sangdo Han, Jeesoo Bang, Seonghan Ryu, and Gary Geunbae Lee. 2015 · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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Open IE as an intermediate structure for semantic tasks
Gabriel Stanovsky, Ido Dagan, and Mausam. 2015 · 2015
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Your paper has been accepted, rejected, or whatever: Automatic generation of scientific paper reviews
Alberto Bartoli, Andrea De Lorenzo, Eric Medvet, and Fabiano Tarlao. 2016 · 2016
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Generation from Abstract Meaning Representation using tree transducers
Jeffrey Flanigan, Chris Dyer, Noah A. Smith, and Jaime Carbonell. 2016 · 2016
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Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O.K. Li. 2016 · 2016
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Globally coherent text generation with neural checklist models
Chloé Kiddon, Luke Zettlemoyer, and Yejin Choi. 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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Design and analysis of the nips 2016 review process
Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, and Ulrike von Luxburg. 2017 · 2016
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Making neural programming architectures generalize via recursion
Jonathon Cai, Richard Shin, and Dawn Song. 2017 · 2017
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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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Attention-over-attention neural networks for reading comprehension
Yiming Cui, Zhipeng Chen, Si Wei, Shijin Wang, Ting Liu, and Guoping Hu. 2017 · 2017
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Learning to generate product reviews from attributes
Li Dong, Shaohan Huang, Furu Wei, Mirella Lapata, Ming Zhou, and Ke Xu. 2017 · 2017
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Creating training corpora for NLG micro-planners
Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini. 2017 · 2017
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Learning to query, reason, and answer questions on ambiguous texts
Xiaoxiao Guo, Tim Klinger, Clemens Rosenbaum, Joseph P Bigus, Murray Campbell, Ban Kawas, Kartik Talamadupula, Gerry Tesauro, and Satinder Singh. 2017 · 2017
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Toward controlled generation of text
Augmenting end-to-end dialogue systems with commonsense knowledge
Tom Young, Erik Cambria, Iti Chaturvedi, Hao Zhou, Subham Biswas, and Minlie Huang. 2018 · 2018
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Commonsense knowledge aware conversation generation with graph attention
Hao Zhou, Tom Young, Minlie Huang, Haizhou Zhao, Jingfang Xu, and Xiaoyan Zhu. 2018 · 2018
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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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Enhancing neural data-to-text generation models with external background knowledge
Shuang Chen, Jinpeng Wang, Xiaocheng Feng, Feng Jiang, Bing Qin, and Chin-Yew Lin. 2019 · 2019
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DeepSentiPeer: Harnessing sentiment in review texts to recommend peer review decisions
Tirthankar Ghosal, Rajeev Verma, Asif Ekbal, and Pushpak Bhattacharyya. 2019 · 2019
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing. 2017 · 2017
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Harvesting creative templates for generating stylistically varied restaurant reviews
Shereen Oraby, Sheideh Homayon, and Marilyn Walker. 2017 · 2017
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Style transfer from non-parallel text by cross-alignment
Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2017 · 2017
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Amortised map inference for image super-resolution
Casper Kaae Sønderby, Jose Caballero, Lucas Theis, Wenzhe Shi, and Ferenc Huszár. 2017 · 2017
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Challenges in data-to-document generation
Sam Wiseman, Stuart Shieber, and Alexander 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 Wang, Chris Wilhelm, Zheng Yuan, Madeleine van Zuylen, and Oren Etzioni. 2018 · 2018
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Transnets for review generation
Rose Catherine and William Cohen. 2018 · 2018
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Enhanced transformer model for data-to-text generation
Li Gong, Josep Crego, and Jean Senellart. 2019 · 2019
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Learning to select, track, and generate for data-to-text
Hayate Iso, Yui Uehara, Tatsuya Ishigaki, Hiroshi Noji, Eiji Aramaki, Ichiro Kobayashi, Yusuke Miyao, Naoaki Okazaki, and Hiroya Takamura. 2019 · 2019
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Text Generation from Knowledge Graphs with Graph Transformers
Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, and Hannaneh Hajishirzi. 2019 · 2019
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Towards controllable and personalized review generation
Pan Li and Alexander Tuzhilin. 2019 · 2019
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Troubling trends in machine learning scholarship
Zachary C Lipton and Jacob Steinhardt. 2019 · 2019
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Knowledge aware conversation generation with explainable reasoning over augmented graphs
Zhibin Liu, Zheng-Yu Niu, Hua Wu, and Haifeng Wang. 2019b · 2019
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Step-by-step: Separating planning from realization in neural data-to-text generation
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Toward a task of feedback comment generation for writing learning
Ryo Nagata. 2019 · 2019
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An encoder with non-sequential dependency for neural data-to-text generation
Feng Nie, Jinpeng Wang, Rong Pan, and Chin-Yew Lin. 2019 · 2019
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An entity-driven framework for abstractive summarization
Eva Sharma, Luyang Huang, Zhe Hu, and Lu Wang. 2019 · 2019
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A topic augmented text generation model: Joint learning of semantics and structural features
Hongyin Tang, Miao Li, and Beihong Jin. 2019 · 2019
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Style transfer for texts: Retrain, report errors, compare with rewrites
Alexey Tikhonov, Viacheslav Shibaev, Aleksander Nagaev, Aigul Nugmanova, and Ivan P. Yamshchikov. 2019 · 2019
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Modeling graph structure in transformer for better AMR-to-text generation
Jie Zhu, Junhui Li, Muhua Zhu, Longhua Qian, Min Zhang, and Guodong Zhou. 2019 · 2019
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Unsupervised opinion summarization as copycat-review generation
Arthur Bražinskas, Mirella Lapata, and Ivan Titov. 2020 · 2020
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Automatic generation of citation texts in scholarly papers: A pilot study
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