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GLEU: Automatic Evaluation of Sentence-Level Fluency
Andrew Mutton
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Mark Dras
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Stephen Wan
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Robert Dale
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Paper Details:
Month: June
Year: 2007
Location: Prague, Czech Republic
Venue:
ACL |
Citations
URL
Choosing the Right Translation: A Syntactically Informed Classification Approach
Simon Zwarts
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Mark Dras
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Exploiting Parse Structures for Native Language Identification
Sze-Meng Jojo Wong
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Mark Dras
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Predicting the Fluency of Text with Shallow Structural Features: Case Studies of Machine Translation and Human-Written Text
Jieun Chae
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Ani Nenkova
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Improving Grammaticality in Statistical Sentence Generation: Introducing a Dependency Spanning Tree Algorithm with an Argument Satisfaction Model
Stephen Wan
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Mark Dras
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Robert Dale
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Cécile Paris
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Can we Evaluate the Quality of Generated Text?
David Hardcastle
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Donia Scott
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Machine Translationness: Machine-likeness in Machine Translation Evaluation
Joaquim Moré
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Salvador Climent
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On Automated Evaluation of Readability of Summaries: Capturing Grammaticality, Focus, Structure and Coherence
Ravikiran Vadlapudi
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Rahul Katragadda
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Automatic Evaluation Method for Machine Translation Using Noun-Phrase Chunking
Hiroshi Echizen-ya
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Kenji Araki
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Judging Grammaticality with Tree Substitution Grammar Derivations
Matt Post
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Learning-based Composite Metrics for Improved Caption Evaluation
Naeha Sharif
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Lyndon White
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Mohammed Bennamoun
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Syed Afaq Ali Shah
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Reassessing the Goals of Grammatical Error Correction: Fluency Instead of Grammaticality
Keisuke Sakaguchi
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Courtney Napoles
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Matt Post
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Joel Tetreault
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Looking up phrase rephrasings via a pivot language
Aurélien Max
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Michael Zock
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Web-based Validation for Contextual Targeted Paraphrasing
Houda Bouamor
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Aurélien Max
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Gabriel Illouz
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Anne Vilnat
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Treat the system like a human student: Automatic naturalness evaluation of generated text without reference texts
Isabel Groves
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Ye Tian
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Ioannis Douratsos
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http://projects.ldc.upenn.edu/TIDES/
http://www.connexor.com
http://trec.nist.gov/data/reuters/
http://nltk.sourceforge.net
Field Of Study
Task
Machine Translation
Language
English
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