@inproceedings{north-etal-2024-multils,
title = "{M}ulti{LS}: An End-to-End Lexical Simplification Framework",
author = "North, Kai and
Ranasinghe, Tharindu and
Shardlow, Matthew and
Zampieri, Marcos",
editor = "Shardlow, Matthew and
Saggion, Horacio and
Alva-Manchego, Fernando and
Zampieri, Marcos and
North, Kai and
{\v{S}}tajner, Sanja and
Stodden, Regina",
booktitle = "Proceedings of the Third Workshop on Text Simplification, Accessibility and Readability (TSAR 2024)",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.tsar-1.1",
pages = "1--11",
abstract = "Lexical Simplification (LS) automatically replaces difficult to read words for easier alternatives while preserving a sentence{'}s original meaning. Several datasets exist for LS and each of them specialize in one or two sub-tasks within the LS pipeline. However, as of this moment, no single LS dataset has been developed that covers all LS sub-tasks. We present MultiLS, the first LS framework that allows for the creation of a multi-task LS dataset. We also present MultiLS-PT, the first dataset created using the MultiLS framework. We demonstrate the potential of MultiLS-PT by carrying out all LS sub-tasks of (1) lexical complexity prediction (LCP), (2) substitute generation, and (3) substitute ranking for Portuguese.",
}
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<abstract>Lexical Simplification (LS) automatically replaces difficult to read words for easier alternatives while preserving a sentence’s original meaning. Several datasets exist for LS and each of them specialize in one or two sub-tasks within the LS pipeline. However, as of this moment, no single LS dataset has been developed that covers all LS sub-tasks. We present MultiLS, the first LS framework that allows for the creation of a multi-task LS dataset. We also present MultiLS-PT, the first dataset created using the MultiLS framework. We demonstrate the potential of MultiLS-PT by carrying out all LS sub-tasks of (1) lexical complexity prediction (LCP), (2) substitute generation, and (3) substitute ranking for Portuguese.</abstract>
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%0 Conference Proceedings
%T MultiLS: An End-to-End Lexical Simplification Framework
%A North, Kai
%A Ranasinghe, Tharindu
%A Shardlow, Matthew
%A Zampieri, Marcos
%Y Shardlow, Matthew
%Y Saggion, Horacio
%Y Alva-Manchego, Fernando
%Y Zampieri, Marcos
%Y North, Kai
%Y Štajner, Sanja
%Y Stodden, Regina
%S Proceedings of the Third Workshop on Text Simplification, Accessibility and Readability (TSAR 2024)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F north-etal-2024-multils
%X Lexical Simplification (LS) automatically replaces difficult to read words for easier alternatives while preserving a sentence’s original meaning. Several datasets exist for LS and each of them specialize in one or two sub-tasks within the LS pipeline. However, as of this moment, no single LS dataset has been developed that covers all LS sub-tasks. We present MultiLS, the first LS framework that allows for the creation of a multi-task LS dataset. We also present MultiLS-PT, the first dataset created using the MultiLS framework. We demonstrate the potential of MultiLS-PT by carrying out all LS sub-tasks of (1) lexical complexity prediction (LCP), (2) substitute generation, and (3) substitute ranking for Portuguese.
%U https://aclanthology.org/2024.tsar-1.1
%P 1-11
Markdown (Informal)
[MultiLS: An End-to-End Lexical Simplification Framework](https://aclanthology.org/2024.tsar-1.1) (North et al., TSAR 2024)
ACL
- Kai North, Tharindu Ranasinghe, Matthew Shardlow, and Marcos Zampieri. 2024. MultiLS: An End-to-End Lexical Simplification Framework. In Proceedings of the Third Workshop on Text Simplification, Accessibility and Readability (TSAR 2024), pages 1–11, Miami, Florida, USA. Association for Computational Linguistics.