@inproceedings{shardlow-etal-2024-bea,
title = "The {BEA} 2024 Shared Task on the Multilingual Lexical Simplification Pipeline",
author = {Shardlow, Matthew and
Alva-Manchego, Fernando and
Batista-Navarro, Riza and
Bott, Stefan and
Calderon Ramirez, Saul and
Cardon, R{\'e}mi and
Fran{\c{c}}ois, Thomas and
Hayakawa, Akio and
Horbach, Andrea and
H{\"u}lsing, Anna and
Ide, Yusuke and
Imperial, Joseph Marvin and
Nohejl, Adam and
North, Kai and
Occhipinti, Laura and
Rojas, Nelson Per{\'e}z and
Raihan, Nishat and
Ranasinghe, Tharindu and
Salazar, Martin Solis and
{\v{S}}tajner, Sanja and
Zampieri, Marcos and
Saggion, Horacio},
editor = {Kochmar, Ekaterina and
Bexte, Marie and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Tack, Ana{\"\i}s and
Yaneva, Victoria and
Yuan, Zheng},
booktitle = "Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bea-1.51",
pages = "571--589",
abstract = "We report the findings of the 2024 Multilingual Lexical Simplification Pipeline shared task. We released a new dataset comprising 5,927 instances of lexical complexity prediction and lexical simplification on common contexts across 10 languages, split into trial (300) and test (5,627). 10 teams participated across 2 tracks and 10 languages with 233 runs evaluated across all systems. Five teams participated in all languages for the lexical complexity prediction task and 4 teams participated in all languages for the lexical simplification task. Teams employed a range of strategies, making use of open and closed source large language models for lexical simplification, as well as feature-based approaches for lexical complexity prediction. The highest scoring team on the combined multilingual data was able to obtain a Pearson{'}s correlation of 0.6241 and an ACC@1@Top1 of 0.3772, both demonstrating that there is still room for improvement on two difficult sub-tasks of the lexical simplification pipeline.",
}
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<abstract>We report the findings of the 2024 Multilingual Lexical Simplification Pipeline shared task. We released a new dataset comprising 5,927 instances of lexical complexity prediction and lexical simplification on common contexts across 10 languages, split into trial (300) and test (5,627). 10 teams participated across 2 tracks and 10 languages with 233 runs evaluated across all systems. Five teams participated in all languages for the lexical complexity prediction task and 4 teams participated in all languages for the lexical simplification task. Teams employed a range of strategies, making use of open and closed source large language models for lexical simplification, as well as feature-based approaches for lexical complexity prediction. The highest scoring team on the combined multilingual data was able to obtain a Pearson’s correlation of 0.6241 and an ACC@1@Top1 of 0.3772, both demonstrating that there is still room for improvement on two difficult sub-tasks of the lexical simplification pipeline.</abstract>
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%0 Conference Proceedings
%T The BEA 2024 Shared Task on the Multilingual Lexical Simplification Pipeline
%A Shardlow, Matthew
%A Alva-Manchego, Fernando
%A Batista-Navarro, Riza
%A Bott, Stefan
%A Calderon Ramirez, Saul
%A Cardon, Rémi
%A François, Thomas
%A Hayakawa, Akio
%A Horbach, Andrea
%A Hülsing, Anna
%A Ide, Yusuke
%A Imperial, Joseph Marvin
%A Nohejl, Adam
%A North, Kai
%A Occhipinti, Laura
%A Rojas, Nelson Peréz
%A Raihan, Nishat
%A Ranasinghe, Tharindu
%A Salazar, Martin Solis
%A Štajner, Sanja
%A Zampieri, Marcos
%A Saggion, Horacio
%Y Kochmar, Ekaterina
%Y Bexte, Marie
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%S Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F shardlow-etal-2024-bea
%X We report the findings of the 2024 Multilingual Lexical Simplification Pipeline shared task. We released a new dataset comprising 5,927 instances of lexical complexity prediction and lexical simplification on common contexts across 10 languages, split into trial (300) and test (5,627). 10 teams participated across 2 tracks and 10 languages with 233 runs evaluated across all systems. Five teams participated in all languages for the lexical complexity prediction task and 4 teams participated in all languages for the lexical simplification task. Teams employed a range of strategies, making use of open and closed source large language models for lexical simplification, as well as feature-based approaches for lexical complexity prediction. The highest scoring team on the combined multilingual data was able to obtain a Pearson’s correlation of 0.6241 and an ACC@1@Top1 of 0.3772, both demonstrating that there is still room for improvement on two difficult sub-tasks of the lexical simplification pipeline.
%U https://aclanthology.org/2024.bea-1.51
%P 571-589
Markdown (Informal)
[The BEA 2024 Shared Task on the Multilingual Lexical Simplification Pipeline](https://aclanthology.org/2024.bea-1.51) (Shardlow et al., BEA 2024)
ACL
- Matthew Shardlow, Fernando Alva-Manchego, Riza Batista-Navarro, Stefan Bott, Saul Calderon Ramirez, Rémi Cardon, Thomas François, Akio Hayakawa, Andrea Horbach, Anna Hülsing, Yusuke Ide, Joseph Marvin Imperial, Adam Nohejl, Kai North, Laura Occhipinti, Nelson Peréz Rojas, Nishat Raihan, Tharindu Ranasinghe, Martin Solis Salazar, et al.. 2024. The BEA 2024 Shared Task on the Multilingual Lexical Simplification Pipeline. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 571–589, Mexico City, Mexico. Association for Computational Linguistics.
