@inproceedings{goswami-etal-2024-gmu,
title = "{GMU} at {MLSP} 2024: Multilingual Lexical Simplification with Transformer Models",
author = "Goswami, Dhiman and
North, Kai and
Zampieri, Marcos",
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-1.57/",
pages = "627--634",
abstract = "This paper presents GMU`s submission to the Multilingual Lexical Simplification Pipeline (MLSP) shared task at the BEA workshop 2024. The task includes Lexical Complexity Prediction (LCP) and Lexical Simplification (LS) sub-tasks across 10 languages. Our submissions achieved rankings ranging from 1st to 5th in LCP and from 1st to 3rd in LS. Our best performing approach for LCP is a weighted ensemble based on Pearson correlation of language specific transformer models trained on all languages combined. For LS, GPT4-turbo zero-shot prompting achieved the best performance."
}
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%0 Conference Proceedings
%T GMU at MLSP 2024: Multilingual Lexical Simplification with Transformer Models
%A Goswami, Dhiman
%A North, Kai
%A Zampieri, Marcos
%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 goswami-etal-2024-gmu
%X This paper presents GMU‘s submission to the Multilingual Lexical Simplification Pipeline (MLSP) shared task at the BEA workshop 2024. The task includes Lexical Complexity Prediction (LCP) and Lexical Simplification (LS) sub-tasks across 10 languages. Our submissions achieved rankings ranging from 1st to 5th in LCP and from 1st to 3rd in LS. Our best performing approach for LCP is a weighted ensemble based on Pearson correlation of language specific transformer models trained on all languages combined. For LS, GPT4-turbo zero-shot prompting achieved the best performance.
%U https://aclanthology.org/2024.bea-1.57/
%P 627-634
Markdown (Informal)
[GMU at MLSP 2024: Multilingual Lexical Simplification with Transformer Models](https://aclanthology.org/2024.bea-1.57/) (Goswami et al., BEA 2024)
ACL