Conference paper Open Access

# Learning Simplifications for Specific Target Audiences

Carolina Scarton; Lucia Specia

### DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<identifier identifierType="DOI">10.5281/zenodo.1410314</identifier>
<creators>
<creator>
<creatorName>Carolina Scarton</creatorName>
<affiliation>University of Sheffield</affiliation>
</creator>
<creator>
<creatorName>Lucia Specia</creatorName>
<affiliation>University of Sheffield</affiliation>
</creator>
</creators>
<titles>
<title>Learning Simplifications for Specific Target Audiences</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2018</publicationYear>
<dates>
<date dateType="Issued">2018-07-15</date>
</dates>
<resourceType resourceTypeGeneral="ConferencePaper"/>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1410314</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1410313</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;Text simplification (TS) is a monolingual text-to-text transformation task where an original (complex) text is transformed into a target (simpler) text. Most recent work is based on sequence-to-sequence neural models similar to those used for machine translation (MT). Different from MT, TS data comprises more elaborate transformations, such as sentence splitting. It can also contain multiple simplifications of the same original text targeting different audiences, such as school grade levels. We explore these two features of TS to build models tailored for specific grade levels. Our approach uses a standard sequence-to-sequence architecture where the original sequence is annotated with information about the target audience and/or the (predicted) type of simplification operation. We show that it outperforms state-of-the-art TS approaches (up to 3 and 12&amp;nbsp; BLEU and SARI points, respectively), including when training data for the specific complex-simple combination of grade levels is not available, i.e. zero-shot learning.&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>

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