@inproceedings{rajpoot-etal-2024-upaya,
title = "Upaya at {A}rabic{NLU} Shared-Task: {A}rabic Lexical Disambiguation using Large Language Models",
author = "Rajpoot, Pawan and
Jindal, Ashvini and
Parikh, Ankur",
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-1.32",
doi = "10.18653/v1/2024.arabicnlp-1.32",
pages = "377--382",
abstract = "Disambiguating a word{'}s intended meaning(sense) in a given context is important in Nat-ural Language Understanding (NLU). WSDaims to determine the correct sense of ambigu-ous words in context. At the same time, LMD(a WSD variation) focuses on disambiguatinglocation mention. Both tasks are vital in Nat-ural Language Processing (NLP) and informa-tion retrieval, as they help correctly interpretand extract information from text. Arabic ver-sion is further challenging because of its mor-phological richness, encompassing a complexinterplay of roots, stems, and affixes. This pa-per describes our solutions to both tasks, em-ploying Llama3 and Cohere-based models un-der Zero-Shot Learning and Re-Ranking, re-spectively. Both the shared tasks were partof the second Arabic Natural Language Pro-cessing Conference co-located with ACL 2024.Overall, we achieved 1st rank in the WSD task(accuracy 78{\%}) and 2nd rank in the LMD task(MRR@1 0.59)",
}
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<abstract>Disambiguating a word’s intended meaning(sense) in a given context is important in Nat-ural Language Understanding (NLU). WSDaims to determine the correct sense of ambigu-ous words in context. At the same time, LMD(a WSD variation) focuses on disambiguatinglocation mention. Both tasks are vital in Nat-ural Language Processing (NLP) and informa-tion retrieval, as they help correctly interpretand extract information from text. Arabic ver-sion is further challenging because of its mor-phological richness, encompassing a complexinterplay of roots, stems, and affixes. This pa-per describes our solutions to both tasks, em-ploying Llama3 and Cohere-based models un-der Zero-Shot Learning and Re-Ranking, re-spectively. Both the shared tasks were partof the second Arabic Natural Language Pro-cessing Conference co-located with ACL 2024.Overall, we achieved 1st rank in the WSD task(accuracy 78%) and 2nd rank in the LMD task(MRR@1 0.59)</abstract>
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%0 Conference Proceedings
%T Upaya at ArabicNLU Shared-Task: Arabic Lexical Disambiguation using Large Language Models
%A Rajpoot, Pawan
%A Jindal, Ashvini
%A Parikh, Ankur
%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 rajpoot-etal-2024-upaya
%X Disambiguating a word’s intended meaning(sense) in a given context is important in Nat-ural Language Understanding (NLU). WSDaims to determine the correct sense of ambigu-ous words in context. At the same time, LMD(a WSD variation) focuses on disambiguatinglocation mention. Both tasks are vital in Nat-ural Language Processing (NLP) and informa-tion retrieval, as they help correctly interpretand extract information from text. Arabic ver-sion is further challenging because of its mor-phological richness, encompassing a complexinterplay of roots, stems, and affixes. This pa-per describes our solutions to both tasks, em-ploying Llama3 and Cohere-based models un-der Zero-Shot Learning and Re-Ranking, re-spectively. Both the shared tasks were partof the second Arabic Natural Language Pro-cessing Conference co-located with ACL 2024.Overall, we achieved 1st rank in the WSD task(accuracy 78%) and 2nd rank in the LMD task(MRR@1 0.59)
%R 10.18653/v1/2024.arabicnlp-1.32
%U https://aclanthology.org/2024.arabicnlp-1.32
%U https://doi.org/10.18653/v1/2024.arabicnlp-1.32
%P 377-382
Markdown (Informal)
[Upaya at ArabicNLU Shared-Task: Arabic Lexical Disambiguation using Large Language Models](https://aclanthology.org/2024.arabicnlp-1.32) (Rajpoot et al., ArabicNLP-WS 2024)
ACL