@inproceedings{abdel-salam-2024-rematchka,
title = "rematchka at {A}rabic{NLU}2024: Evaluating Large Language Models for {A}rabic Word Sense and Location Sense Disambiguation",
author = "Abdel-Salam, Reem",
editor = "Habash, Nizar and
Bouamor, Houda and
Eskander, Ramy and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Abdelali, Ahmed and
Touileb, Samia and
Hamed, Injy and
Onaizan, Yaser and
Alhafni, Bashar and
Antoun, Wissam and
Khalifa, Salam and
Haddad, Hatem and
Zitouni, Imed and
AlKhamissi, Badr and
Almatham, Rawan and
Mrini, Khalil",
booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.arabicnlp-1.33",
pages = "383--392",
abstract = "Natural Language Understanding (NLU) plays a vital role in Natural Language Processing (NLP) by facilitating semantic interactions. Arabic, with its diverse morphology, poses a challenge as it allows multiple interpretations of words, leading to potential misunderstandings and errors in NLP applications. In this paper, we present our approach for tackling Arabic NLU shared tasks for word sense disambiguation (WSD) and location mention disambiguation (LMD). Various approaches have been investigated from zero-shot inference of large language models (LLMs) to fine-tuning of pre-trained language models (PLMs). The best approach achieved 57{\%} on WSD task ranking third place, while for the LMD task, our best systems achieved 94{\%} MRR@1 ranking first place.",
}
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<abstract>Natural Language Understanding (NLU) plays a vital role in Natural Language Processing (NLP) by facilitating semantic interactions. Arabic, with its diverse morphology, poses a challenge as it allows multiple interpretations of words, leading to potential misunderstandings and errors in NLP applications. In this paper, we present our approach for tackling Arabic NLU shared tasks for word sense disambiguation (WSD) and location mention disambiguation (LMD). Various approaches have been investigated from zero-shot inference of large language models (LLMs) to fine-tuning of pre-trained language models (PLMs). The best approach achieved 57% on WSD task ranking third place, while for the LMD task, our best systems achieved 94% MRR@1 ranking first place.</abstract>
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%0 Conference Proceedings
%T rematchka at ArabicNLU2024: Evaluating Large Language Models for Arabic Word Sense and Location Sense Disambiguation
%A Abdel-Salam, Reem
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Eskander, Ramy
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Abdelali, Ahmed
%Y Touileb, Samia
%Y Hamed, Injy
%Y Onaizan, Yaser
%Y Alhafni, Bashar
%Y Antoun, Wissam
%Y Khalifa, Salam
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y AlKhamissi, Badr
%Y Almatham, Rawan
%Y Mrini, Khalil
%S Proceedings of The Second Arabic Natural Language Processing Conference
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F abdel-salam-2024-rematchka
%X Natural Language Understanding (NLU) plays a vital role in Natural Language Processing (NLP) by facilitating semantic interactions. Arabic, with its diverse morphology, poses a challenge as it allows multiple interpretations of words, leading to potential misunderstandings and errors in NLP applications. In this paper, we present our approach for tackling Arabic NLU shared tasks for word sense disambiguation (WSD) and location mention disambiguation (LMD). Various approaches have been investigated from zero-shot inference of large language models (LLMs) to fine-tuning of pre-trained language models (PLMs). The best approach achieved 57% on WSD task ranking third place, while for the LMD task, our best systems achieved 94% MRR@1 ranking first place.
%U https://aclanthology.org/2024.arabicnlp-1.33
%P 383-392
Markdown (Informal)
[rematchka at ArabicNLU2024: Evaluating Large Language Models for Arabic Word Sense and Location Sense Disambiguation](https://aclanthology.org/2024.arabicnlp-1.33) (Abdel-Salam, ArabicNLP-WS 2024)
ACL