@inproceedings{cripwell-etal-2023-simplicity,
title = "Simplicity Level Estimate ({SLE}): A Learned Reference-Less Metric for Sentence Simplification",
author = {Cripwell, Liam and
Legrand, Jo{\"e}l and
Gardent, Claire},
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.739",
doi = "10.18653/v1/2023.emnlp-main.739",
pages = "12053--12059",
abstract = "Automatic evaluation for sentence simplification remains a challenging problem. Most popular evaluation metrics require multiple high-quality references {--} something not readily available for simplification {--} which makes it difficult to test performance on unseen domains. Furthermore, most existing metrics conflate simplicity with correlated attributes such as fluency or meaning preservation. We propose a new learned evaluation metric {---} SLE {---} which focuses on simplicity, outperforming almost all existing metrics in terms of correlation with human judgements.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="cripwell-etal-2023-simplicity">
<titleInfo>
<title>Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification</title>
</titleInfo>
<name type="personal">
<namePart type="given">Liam</namePart>
<namePart type="family">Cripwell</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joël</namePart>
<namePart type="family">Legrand</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Claire</namePart>
<namePart type="family">Gardent</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Houda</namePart>
<namePart type="family">Bouamor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Juan</namePart>
<namePart type="family">Pino</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kalika</namePart>
<namePart type="family">Bali</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Singapore</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Automatic evaluation for sentence simplification remains a challenging problem. Most popular evaluation metrics require multiple high-quality references – something not readily available for simplification – which makes it difficult to test performance on unseen domains. Furthermore, most existing metrics conflate simplicity with correlated attributes such as fluency or meaning preservation. We propose a new learned evaluation metric — SLE — which focuses on simplicity, outperforming almost all existing metrics in terms of correlation with human judgements.</abstract>
<identifier type="citekey">cripwell-etal-2023-simplicity</identifier>
<identifier type="doi">10.18653/v1/2023.emnlp-main.739</identifier>
<location>
<url>https://aclanthology.org/2023.emnlp-main.739</url>
</location>
<part>
<date>2023-12</date>
<extent unit="page">
<start>12053</start>
<end>12059</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification
%A Cripwell, Liam
%A Legrand, Joël
%A Gardent, Claire
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F cripwell-etal-2023-simplicity
%X Automatic evaluation for sentence simplification remains a challenging problem. Most popular evaluation metrics require multiple high-quality references – something not readily available for simplification – which makes it difficult to test performance on unseen domains. Furthermore, most existing metrics conflate simplicity with correlated attributes such as fluency or meaning preservation. We propose a new learned evaluation metric — SLE — which focuses on simplicity, outperforming almost all existing metrics in terms of correlation with human judgements.
%R 10.18653/v1/2023.emnlp-main.739
%U https://aclanthology.org/2023.emnlp-main.739
%U https://doi.org/10.18653/v1/2023.emnlp-main.739
%P 12053-12059
Markdown (Informal)
[Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification](https://aclanthology.org/2023.emnlp-main.739) (Cripwell et al., EMNLP 2023)
ACL