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	title={Syntax-aware multi-sense word embeddings for deep compositional models of meaning},
	author={Cheng, Jianpeng and Kartsaklis, Dimitri},
	journal={arXiv preprint arXiv:1508.02354},
	year={2015}
}

@inproceedings{sulem2018Semantic,
	author = {Sulem, Elior and Abend, Omri and Rappoport, Ari},
	title = {Semantic structural annotation for text simplification},
	booktitle = {Proc. of NAACL},
	note = {To appear},
	year = {2018}}

@inproceedings{grundkiewicz2015human,
	title={Human Evaluation of Grammatical Error Correction Systems.},
	author={Grundkiewicz, Roman and Junczys-Dowmunt, Marcin and Gillian, Edward and others},
	booktitle={EMNLP},
	pages={461--470},
	year={2015}
}
@article{Xu-EtAl:2016:TACL,
	author = {Wei Xu and Courtney Napoles and Ellie Pavlick and Quanze Chen and Chris Callison-Burch},
	title = {Optimizing Statistical Machine Translation for Text Simplification},
	journal = {Transactions of the Association for Computational Linguistics},
	volume = {4},
	year = {2016},
	url = {https://cocoxu.github.io/publications/tacl2016-smt-simplification.pdf},
	pages = {401--415}
}
@Article{napoles2016gleu,
	author    = {Napoles, Courtney  and  Sakaguchi, Keisuke  and  Post, Matt  and  Tetreault, Joel},
	title     = {{GLEU} Without Tuning},
	journal   = {eprint arXiv:1605.02592 [cs.CL]},
	year      = {2016},
	url       = {http://arxiv.org/abs/1605.02592}
}

@InProceedings{zhang-lapata:2017:EMNLP2017,
author    = {Zhang, Xingxing  and  Lapata, Mirella},
title     = {Sentence Simplification with Deep Reinforcement Learning},
booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
month     = {September},
year      = {2017},
address   = {Copenhagen, Denmark},
publisher = {Association for Computational Linguistics},
pages     = {584--594},
abstract  = {Sentence simplification aims to make sentences easier to read and
understand. Most recent approaches draw on insights from machine
translation to learn simplification rewrites from monolingual
corpora of complex and simple sentences. We address the
simplification problem with an encoder-decoder model coupled with a
deep reinforcement learning framework. Our model, which we call {\sc
Dress} (as shorthand for {\bf D}eep {\bf RE}inforcement {\bf
S}entence {\bf S}implification), explores the space of possible
simplifications while learning to optimize a reward function that
encourages outputs which are simple, fluent, and preserve the
meaning of the input. Experiments on three datasets demonstrate that
our model outperforms competitive simplification
systems.},
url       = {https://www.aclweb.org/anthology/D17-1062}
}
@InProceedings{narayan-gardent:2016:INLG,
	author    = {Narayan, Shashi  and  Gardent, Claire},
	title     = {Unsupervised Sentence Simplification Using Deep Semantics},
	booktitle = {Proceedings of the 9th International Natural Language Generation conference},
	month     = {September 5-8},
	year      = {2016},
	address   = {Edinburgh, UK},
	publisher = {Association for Computational Linguistics},
	pages     = {111--120},
	url       = {http://anthology.aclweb.org/W16-6620}
}
@InProceedings{narayan-gardent:2014:P14-1,
	author    = {Narayan, Shashi  and  Gardent, Claire},
	title     = {Hybrid Simplification using Deep Semantics and Machine Translation},
	booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
	month     = {June},
	year      = {2014},
	address   = {Baltimore, Maryland},
	publisher = {Association for Computational Linguistics},
	pages     = {435--445},
	url       = {http://www.aclweb.org/anthology/P14-1041}
}

@article{xu2015problems,
	title={Problems in current text simplification research: New data can help},
	author={Xu, Wei and Callison-Burch, Chris and Napoles, Courtney},
	journal={Transactions of the Association for Computational Linguistics},
	volume={3},
	pages={283--297},
	year={2015}
}
@inproceedings{inderjeet2009summarization,
		title={Summarization evaluation: an overview},
	author={Mani, Inderjeet},
	booktitle={Proceedings of the NTCIR Workshop},
	volume={2},
	year={2009}
}
@inproceedings{sulem2017summarization,
	author = {Sulem, Elior and Abend, Omri and Rappoport, Ari},
	title = {Simple and Effective Text Simplification using Semantic and Neural Methods},
	booktitle = {Proc. of ACL},
	note = {To appear},
	year = {2018}
}
@inproceedings{nisioi2017exploring,
	title={Exploring neural text simplification models},
	author={Nisioi, Sergiu and {\v{S}}tajner, Sanja and Ponzetto, Simone Paolo and Dinu, Liviu P},
	booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
	volume={2},
	pages={85--91},
	year={2017}
}
@InProceedings{alvamanchego-EtAl:2017:I17-1,
	author    = {Alva-Manchego, Fernando  and  Bingel, Joachim  and  Paetzold, Gustavo  and  Scarton, Carolina  and  Specia, Lucia},
	title     = {Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs},
	booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
	month     = {November},
	year      = {2017},
	address   = {Taipei, Taiwan},
	publisher = {Asian Federation of Natural Language Processing},
	pages     = {295--305},
	abstract  = {Current research in text simplification has been hampered by two central
	problems: (i) the small amount of high-quality parallel simplification data
	available, and (ii) the lack of explicit annotations of simplification
	operations, such as deletions or substitutions, on existing data. While the
	recently introduced Newsela corpus has alleviated the first problem,
	simplifications still need to be learned directly from parallel text using
	black-box, end-to-end approaches rather than from explicit annotations. These
	complex-simple parallel sentence pairs often differ to such a high degree that
	generalization becomes difficult.  End-to-end models also make it hard to
	interpret what is actually learned from data. We propose a method that
	decomposes the task of TS into its sub-problems. We devise a way to
	automatically identify operations in a parallel corpus and introduce a
	sequence-labeling approach based on these annotations. Finally, we provide
	insights on the types of transformations that different approaches can model.},
	url       = {http://www.aclweb.org/anthology/I17-1030}
}

@InProceedings{banarescu-EtAl:2013:LAW7-ID,
	author    = {Banarescu, Laura  and  Bonial, Claire  and  Cai, Shu  and  Georgescu, Madalina  and  Griffitt, Kira  and  Hermjakob, Ulf  and  Knight, Kevin  and  Koehn, Philipp  and  Palmer, Martha  and  Schneider, Nathan},
	title     = {Abstract Meaning Representation for Sembanking},
	booktitle = {Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse},
	month     = {August},
	year      = {2013},
	address   = {Sofia, Bulgaria},
	publisher = {Association for Computational Linguistics},
	pages     = {178--186}
}
@InProceedings{bryant-felice-briscoe:2017:Long,
	author    = {Bryant, Christopher  and  Felice, Mariano  and  Briscoe, Ted},
	title     = {Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction},
	booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
	month     = {July},
	year      = {2017},
	address   = {Vancouver, Canada},
	publisher = {Association for Computational Linguistics},
	pages     = {793--805},
	abstract  = {Until now, error type performance for Grammatical Error Correction (GEC)
	systems could only be measured in terms of recall because system output is not
	annotated. To overcome this problem, we introduce ERRANT, a grammatical ERRor
	ANnotation Toolkit designed to automatically extract edits from parallel
	original and corrected sentences and classify them according to a new,
	dataset-agnostic, rule-based framework. This not only facilitates error type
	evaluation at different levels of granularity, but can also be used to reduce
	annotator workload and standardise existing GEC datasets. Human experts rated
	the automatic edits as ``Good'' or ``Acceptable'' in at least 95\% of cases, so
	we applied ERRANT to the system output of the CoNLL-2014 shared task to carry
	out a detailed error type analysis for the first time.},
	url       = {http://aclweb.org/anthology/P17-1074}
}

@inproceedings{asano2017reference,
	title={Reference-based Metrics can be Replaced with Reference-less Metrics in Evaluating Grammatical Error Correction Systems},
	author={Asano, Hiroki and Mizumoto, Tomoya and Inui, Kentaro},
	booktitle={Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
	volume={2},
	pages={343--348},
	year={2017}
}
@inproceedings{choshen2018conservatism,
	author = {Choshen, Leshem and Abend, Omri}, 
	title = {Inherent Biases in Reference-based Evaluation for Grammatical Error Correction and Text Simplification}, 
	booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
	year={2018}
}
@inproceedings{choshen2018usim,
	author = {Choshen, Leshem and Abend, Omri}, 
	title = {Reference-less Measure of Faithfulness for Grammatical Error Correction}, 
	booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, 
	year={2018}
	}
@inproceedings{choshen2018maege,
	author = {Choshen, Leshem and Abend, Omri}, 
	title = {Automatic Metric Validation for Grammatical Error Correction}, 
	booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
	year={2018}
}
@inproceedings{dreyer2012hyter,
	title={Hyter: Meaning-equivalent semantics for translation evaluation},
	author={Dreyer, Markus and Marcu, Daniel},
	booktitle={Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
	pages={162--171},
	year={2012},
	organization={Association for Computational Linguistics}
}