<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ferres-saggion-2022-alexsis">
    <titleInfo>
        <title>ALEXSIS: A Dataset for Lexical Simplification in Spanish</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Daniel</namePart>
        <namePart type="family">Ferrés</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Horacio</namePart>
        <namePart type="family">Saggion</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Thirteenth Language Resources and Evaluation Conference</title>
        </titleInfo>
        <originInfo>
            <publisher>European Language Resources Association</publisher>
            <place>
                <placeTerm type="text">Marseille, France</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Lexical Simplification is the process of reducing the lexical complexity of a text by replacing difficult words with easier to read (or understand) expressions while preserving the original information and meaning. In this paper we introduce ALEXSIS, a new dataset for this task, and we use ALEXSIS to benchmark Lexical Simplification systems in Spanish. The paper describes the evaluation of three kind of approaches to Lexical Simplification, a thesaurus-based approach, a single transformers-based approach, and a combination of transformers. We also report state of the art results on a previous Lexical Simplification dataset for Spanish.</abstract>
    <identifier type="citekey">ferres-saggion-2022-alexsis</identifier>
    <location>
        <url>https://aclanthology.org/2022.lrec-1.383</url>
    </location>
    <part>
        <date>2022-06</date>
        <extent unit="page">
            <start>3582</start>
            <end>3594</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T ALEXSIS: A Dataset for Lexical Simplification in Spanish
%A Ferrés, Daniel
%A Saggion, Horacio
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F ferres-saggion-2022-alexsis
%X Lexical Simplification is the process of reducing the lexical complexity of a text by replacing difficult words with easier to read (or understand) expressions while preserving the original information and meaning. In this paper we introduce ALEXSIS, a new dataset for this task, and we use ALEXSIS to benchmark Lexical Simplification systems in Spanish. The paper describes the evaluation of three kind of approaches to Lexical Simplification, a thesaurus-based approach, a single transformers-based approach, and a combination of transformers. We also report state of the art results on a previous Lexical Simplification dataset for Spanish.
%U https://aclanthology.org/2022.lrec-1.383
%P 3582-3594

Markdown (Informal)

[ALEXSIS: A Dataset for Lexical Simplification in Spanish](https://aclanthology.org/2022.lrec-1.383) (Ferrés & Saggion, LREC 2022)

• ALEXSIS: A Dataset for Lexical Simplification in Spanish (Ferrés & Saggion, LREC 2022)

ACL

• Daniel Ferrés and Horacio Saggion. 2022. ALEXSIS: A Dataset for Lexical Simplification in Spanish. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3582–3594, Marseille, France. European Language Resources Association.