@inproceedings{stajner-etal-2017-sentence,
title = "Sentence Alignment Methods for Improving Text Simplification Systems",
author = "{\v{S}}tajner, Sanja and
Franco-Salvador, Marc and
Ponzetto, Simone Paolo and
Rosso, Paolo and
Stuckenschmidt, Heiner",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2016",
doi = "10.18653/v1/P17-2016",
pages = "97--102",
abstract = "We provide several methods for sentence-alignment of texts with different complexity levels. Using the best of them, we sentence-align the Newsela corpora, thus providing large training materials for automatic text simplification (ATS) systems. We show that using this dataset, even the standard phrase-based statistical machine translation models for ATS can outperform the state-of-the-art ATS systems.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="stajner-etal-2017-sentence">
<titleInfo>
<title>Sentence Alignment Methods for Improving Text Simplification Systems</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sanja</namePart>
<namePart type="family">Štajner</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Marc</namePart>
<namePart type="family">Franco-Salvador</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Simone</namePart>
<namePart type="given">Paolo</namePart>
<namePart type="family">Ponzetto</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Paolo</namePart>
<namePart type="family">Rosso</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Heiner</namePart>
<namePart type="family">Stuckenschmidt</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Regina</namePart>
<namePart type="family">Barzilay</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Min-Yen</namePart>
<namePart type="family">Kan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vancouver, Canada</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>We provide several methods for sentence-alignment of texts with different complexity levels. Using the best of them, we sentence-align the Newsela corpora, thus providing large training materials for automatic text simplification (ATS) systems. We show that using this dataset, even the standard phrase-based statistical machine translation models for ATS can outperform the state-of-the-art ATS systems.</abstract>
<identifier type="citekey">stajner-etal-2017-sentence</identifier>
<identifier type="doi">10.18653/v1/P17-2016</identifier>
<location>
<url>https://aclanthology.org/P17-2016</url>
</location>
<part>
<date>2017-07</date>
<extent unit="page">
<start>97</start>
<end>102</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Sentence Alignment Methods for Improving Text Simplification Systems
%A Štajner, Sanja
%A Franco-Salvador, Marc
%A Ponzetto, Simone Paolo
%A Rosso, Paolo
%A Stuckenschmidt, Heiner
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F stajner-etal-2017-sentence
%X We provide several methods for sentence-alignment of texts with different complexity levels. Using the best of them, we sentence-align the Newsela corpora, thus providing large training materials for automatic text simplification (ATS) systems. We show that using this dataset, even the standard phrase-based statistical machine translation models for ATS can outperform the state-of-the-art ATS systems.
%R 10.18653/v1/P17-2016
%U https://aclanthology.org/P17-2016
%U https://doi.org/10.18653/v1/P17-2016
%P 97-102
Markdown (Informal)
[Sentence Alignment Methods for Improving Text Simplification Systems](https://aclanthology.org/P17-2016) (Štajner et al., ACL 2017)
ACL
- Sanja Štajner, Marc Franco-Salvador, Simone Paolo Ponzetto, Paolo Rosso, and Heiner Stuckenschmidt. 2017. Sentence Alignment Methods for Improving Text Simplification Systems. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 97–102, Vancouver, Canada. Association for Computational Linguistics.