<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="scalercio-etal-2024-enhancing">
    <titleInfo>
        <title>Enhancing Sentence Simplification in Portuguese: Leveraging Paraphrases, Context, and Linguistic Features</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Arthur</namePart>
        <namePart type="family">Scalercio</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Maria</namePart>
        <namePart type="family">Finatto</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Aline</namePart>
        <namePart type="family">Paes</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics ACL 2024</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Lun-Wei</namePart>
            <namePart type="family">Ku</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Andre</namePart>
            <namePart type="family">Martins</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Vivek</namePart>
            <namePart type="family">Srikumar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Bangkok, Thailand and virtual meeting</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Automatic text simplification focuses on transforming texts into a more comprehensible version without sacrificing their precision. However, automatic methods usually require (paired) datasets that can be rather scarce in languages other than English. This paper presents a new approach to automatic sentence simplification that leverages paraphrases, context, and linguistic attributes to overcome the absence of paired texts in Portuguese.We frame the simplification problem as a textual style transfer task and learn a style representation using the sentences around the target sentence in the document and its linguistic attributes. Moreover, unlike most unsupervised approaches that require style-labeled training data, we fine-tune strong pre-trained models using sentence-level paraphrases instead of annotated data. Our experiments show that our model achieves remarkable results, surpassing the current state-of-the-art (BART+ACCESS) while competitively matching a Large Language Model.</abstract>
    <identifier type="citekey">scalercio-etal-2024-enhancing</identifier>
    <location>
        <url>https://aclanthology.org/2024.findings-acl.895</url>
    </location>
    <part>
        <date>2024-08</date>
        <extent unit="page">
            <start>15076</start>
            <end>15091</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Enhancing Sentence Simplification in Portuguese: Leveraging Paraphrases, Context, and Linguistic Features
%A Scalercio, Arthur
%A Finatto, Maria
%A Paes, Aline
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F scalercio-etal-2024-enhancing
%X Automatic text simplification focuses on transforming texts into a more comprehensible version without sacrificing their precision. However, automatic methods usually require (paired) datasets that can be rather scarce in languages other than English. This paper presents a new approach to automatic sentence simplification that leverages paraphrases, context, and linguistic attributes to overcome the absence of paired texts in Portuguese.We frame the simplification problem as a textual style transfer task and learn a style representation using the sentences around the target sentence in the document and its linguistic attributes. Moreover, unlike most unsupervised approaches that require style-labeled training data, we fine-tune strong pre-trained models using sentence-level paraphrases instead of annotated data. Our experiments show that our model achieves remarkable results, surpassing the current state-of-the-art (BART+ACCESS) while competitively matching a Large Language Model.
%U https://aclanthology.org/2024.findings-acl.895
%P 15076-15091

Markdown (Informal)

[Enhancing Sentence Simplification in Portuguese: Leveraging Paraphrases, Context, and Linguistic Features](https://aclanthology.org/2024.findings-acl.895) (Scalercio et al., Findings 2024)

• Enhancing Sentence Simplification in Portuguese: Leveraging Paraphrases, Context, and Linguistic Features (Scalercio et al., Findings 2024)

ACL

• Arthur Scalercio, Maria Finatto, and Aline Paes. 2024. Enhancing Sentence Simplification in Portuguese: Leveraging Paraphrases, Context, and Linguistic Features. In Findings of the Association for Computational Linguistics ACL 2024, pages 15076–15091, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.