Conference paper Open Access

# Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs

Fernando Alva-Manchego; Joachim Bingel; Gustavo Henrique Paetzold; Carolina Scarton; Lucia Specia

### DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
<identifier identifierType="DOI">10.5281/zenodo.1042505</identifier>
<creators>
<creator>
<creatorName>Fernando Alva-Manchego</creatorName>
<affiliation>University of Sheffield</affiliation>
</creator>
<creator>
<creatorName>Joachim Bingel</creatorName>
<affiliation>University of Copenhagen</affiliation>
</creator>
<creator>
<creatorName>Gustavo Henrique Paetzold</creatorName>
<affiliation>University of Sheffield</affiliation>
</creator>
<creator>
<creatorName>Carolina Scarton</creatorName>
<affiliation>University</affiliation>
</creator>
<creator>
<creatorName>Lucia Specia</creatorName>
<affiliation>Univer</affiliation>
</creator>
</creators>
<titles>
<title>Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2017</publicationYear>
<dates>
<date dateType="Issued">2017-11-27</date>
</dates>
<language>en</language>
<resourceType resourceTypeGeneral="ConferencePaper"/>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1042505</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1042504</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/h2020-simpatico-692819</relatedIdentifier>
</relatedIdentifiers>
<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">&lt;p&gt;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&amp;nbsp;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. &amp;nbsp;End-to-end models also make it hard to interpret what is actually learned from data. &amp;nbsp;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.&lt;/p&gt;</description>
</descriptions>
<fundingReferences>
<fundingReference>
<funderName>European Commission</funderName>
<funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
<awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/692819/">692819</awardNumber>
<awardTitle>SIMplifying the interaction with Public Administration Through Information technology for Citizens and cOmpanies</awardTitle>
</fundingReference>
</fundingReferences>
</resource>

36
28
views