Fetching the paper…
Reading the bibliography…
We propose USim, a semantic measure for Grammatical Error Correction (GEC) that measures the semantic faithfulness of the output to the source, thereby complementing existing reference-less measures (RLMs) for measuring the output's grammaticality.
On the variability of interlanguage systems
Elaine Tarone. 1983 · 1983
Earlier work this paper cites.
System and variability in interlanguage syntax
Thorn Huebner. 1985 · 1985
Earlier work this paper cites.
Parsing ungrammatical input: an evaluation procedure
Jennifer Foster. 2004 · 2004
Earlier work this paper cites.
Unsupervised evaluation of parser robustness
Johnny Bigert, Jonas Sjöbergh, Ola Knutsson, and Magnus Sahlgren. 2005 · 2005
Earlier work this paper cites.
Measuring mt adequacy using latent semantic analysis
Florence Reeder. 2006 · 2006
Earlier work this paper cites.
A re-examination of machine learning approaches for sentence-level mt evaluation
Joshua Albrecht and Rebecca Hwa. 2007 · 2007
Earlier work this paper cites.
Native judgments of non-native usage: Experiments in preposition error detection
Joel R Tetreault and Martin Chodorow. 2008 · 2008
Earlier work this paper cites.
Estimating the sentence-level quality of machine translation systems
Lucia Specia, Marco Turchi, Nicola Cancedda, Marc Dymetman, and Nello Cristianini. 2009 · 2009
Earlier work this paper cites.
Machine translation evaluation versus quality estimation
Lucia Specia, Dhwaj Raj, and Marco Turchi. 2010 · 2010
Earlier work this paper cites.
They can help: Using crowdsourcing to improve the evaluation of grammatical error detection systems
Nitin Madnani, Joel Tetreault, Martin Chodorow, and Alla Rozovskaya. 2011 · 2011
Earlier work this paper cites.
Problems in evaluating grammatical error detection systems
Martin Chodorow, Markus Dickinson, Ross Israel, and Joel R Tetreault. 2012 · 2012
Earlier work this paper cites.
Better evaluation for grammatical error correction
Daniel Dahlmeier and Hwee Tou Ng. 2012 · 2012
Cited alongside, same era.
Defining syntax for learner language annotation
Marwa Ragheb and Markus Dickinson. 2012 · 2012
Cited alongside, same era.
Universal conceptual cognitive annotation (ucca)
Omri Abend and Ari Rappoport. 2013 · 2013
Cited alongside, same era.
Abstract meaning representation for sembanking
Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. 2013 · 2013
Cited alongside, same era.
Building a large annotated corpus of learner english: The nus corpus of learner english
Daniel Dahlmeier, Hwee Tou Ng, and Siew Mei Wu. 2013 · 2013
Cited alongside, same era.
Building a large annotated corpus of learner english: The nus corpus of learner english
Daniel Dahlmeier, Hwee Tou Ng, and Siew Mei Wu. 2013 · 2013
Conceptual annotations preserve structure across translations: A french-english case study
Elior Sulem, Omri Abend, and Ari Rappoport. 2015 · 2015
Later among the works it cites.
Universal dependencies for learner english
Yevgeni Berzak, Jessica Kenney, Carolyn Spadine, Jing Xian Wang, Lucia Lam, Keiko Sophie Mori, Sebastian Garza, and Boris Katz. 2016 · 2016
Later among the works it cites.
Hume: Human ucca-based evaluation of machine translation
Alexandra Birch, Omri Abend, Ondřej Bojar, and Barry Haddow. 2016 · 2016
Later among the works it cites.
Phrase structure annotation and parsing for learner english
Ryo Nagata and Keisuke Sakaguchi. 2016 · 2016
Later among the works it cites.
There’s no comparison: Reference-less evaluation metrics in grammatical error correction
Courtney Napoles, Keisuke Sakaguchi, and Joel Tetreault. 2016 · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Xmeant: Better semantic mt evaluation without reference translations
Chi-kiu Lo, Meriem Beloucif, Markus Saers, and Dekai Wu. 2014 · 2014
Cited alongside, same era.
Adequacy-fluency metrics: Evaluating mt in the continuous space model framework
Rafael E Banchs, Luis F D’Haro, and Haizhou Li. 2015 · 2015
Cited alongside, same era.
How far are we from fully automatic high quality grammatical error correction?
Christopher Bryant and Hwee Tou Ng. 2015 · 2015
Cited alongside, same era.
Towards a standard evaluation method for grammatical error detection and correction
Mariano Felice and Ted Briscoe. 2015 · 2015
Cited alongside, same era.
Ground truth for grammatical error correction metrics
Courtney Napoles, Keisuke Sakaguchi, Matt Post, and Joel Tetreault. 2015 · 2015
Cited alongside, same era.
Automatic metric validation for grammatical error correction
Leshem Choshen and Omri Abend. 2018a
Cited in the paper.
Hiroki Asano, Tomoya Mizumoto, and Kentaro Inui. 2017 · 2017
Later among the works it cites.
A transition-based directed acyclic graph parser for ucca
Daniel Hershcovich, Omri Abend, and Ari Rappoport. 2017 · 2017
Later among the works it cites.
Jfleg: A fluency corpus and benchmark for grammatical error correction
Courtney Napoles, Keisuke Sakaguchi, and Joel Tetreault. 2017 · 2017
Later among the works it cites.
Grammatical error correction with neural reinforcement learning
Keisuke Sakaguchi, Matt Post, and Benjamin Van Durme. 2017 · 2017
Later among the works it cites.
Semantic structural annotation for text simplification
Elior Sulem, Omri Abend, and Ari Rappoport. 2018 · 2018
Closest in time.