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BLEU is widely considered to be an informative metric for text-to-text generation, including Text Simplification (TS).
Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit
Jacob Cohen. 1968 · 1968
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Derivation of new readability formulas (automated readability index, fog count and Flesch reading ease formula) for Navy enlisted personnel
J. Peter Kincaid, Robert P. Fishburne Jr., Richard L. Rogers, and Brad S. Chissom. 1975 · 1975
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Facilitating reading comprehension through text structure manipulation
Jana M. Mason and Janet R. Kendall. 1979 · 1979
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Motivations and methods for sentence simplification
Raman Chandrasekar, Christine Doran, and Bangalore Srinivas. 1996 · 1996
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NLTK: the natural language toolkit
Edward Loper and Steven Bird. 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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Experiments with discourse-level choices and readability
Sandra Williams, Ehud Reiter, and Liesl Osman. 2003 · 2003
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Re-evaluating the role of BLEU in machine translation
Chris Callison-Burch, Miles Osborne, and Philipp Koehn. 2006 · 2006
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Manual and automatic evaluation of machine translation between European languages
Philipp Koehn and Christof Monz. 2006 · 2006
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Hong Sun and Ming Zhou. 2012 · 2008
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Y. Albert Park and Roger Levy. 2011 · 2011
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Learning to simplify sentences with quasi-synchronous grammar and integer programming
Kristian Woodsend and Mirella Lapata. 2011 · 2011
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Sentence simplification by monolingual machine translation
Sander Wubben, Antal van den Bosch, and Emiel Krahmer. 2012 · 2012
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Exploring the effects of sentence simplification on Hindi to English Machine Translation systems
Kshitij Mishra, Ankush Soni, Rahul Sharma, and Dipti Misra Sharma. 2014 · 2014
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Hybrid simplification using deep semantics and machine translation
A deeper exploration of the standard PB-SMT approach to text simplification and its evaluation
Sanja Štajner, Hannah Bechara, and Horacio Saggion. 2015 · 2015
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Unsupervised sentence simplification using deep semantics
Shashi Narayan and Claire Gardent. 2016 · 2016
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An empirical analysis of formality in online communication
Ellie Pavlick and Joel Tetreault. 2016 · 2016
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Optimizing statistical machine translation for text simplification
Wei Xu, Courtney Napoles, Ellie Pavlick, Quanze Chen, and Chris Callison-Burch. 2016 · 2016
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A semantic relevance based neural network for text summarization and text simplification
Shuming Ma and Xu Sun. 2017 · 2017
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Split and rephrase
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Shashi Narayan and Claire Gardent. 2014 · 2014
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Exploring neural text simplification models
Sergiu Nisioi, Sanja Štajner, Simone Paolo Ponzetto, and Liviu P. Dinu. 2017 · 2014
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Hybrid text simplification using synchronous dependency grammars with hand-written and automatically harvested rules
Advaith Siddharthan and M. A. Angrosh. 2014 · 2014
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One step closer to automatic evaluation of text simplification systems
Sanja Štajner, Ruslan Mitkov, and Horacio Saggion. 2014 · 2014
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Re-evaluating automatic summarization with BLEU and 192 shades of ROUGE
Yvette Graham. 2015 · 2015
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Detecting content-heavy sentences: A cross-language case study
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Semantic structural evaluation for text simplification
Elior Sulem, Omri Abend, and Ari Rappoport. 2018a
Cited in the paper.
Shashi Narayan, Claire Gardent, Shay B. Cohen, and Anastasia Shimorina. 2017 · 2017
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Sentence simplification with deep reinforcement learning
Xingxing Zhang and Mirella Lapata. 2017 · 2017
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A constrained sequence-to-sequence neural model for sentence simplification
Yaoyuan Zhang, Zhenxu Ye, Dongyan Zhao, and Rui Yan. 2017 · 2017
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Split and rephrase: Better evaluation and a stronger baseline
Roee Aharoni and Yoav Goldberg. 2018 · 2018
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Moses: open source toolkit for statistical machine translation
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