ASOS at KSAA-CAD 2024: One Embedding is All You Need for Your Dictionary
Serry Sibaee, Abdullah Alharbi, Samar Ahmad, Omer Nacar, Anis Koubaa, Lahouari Ghouti
Abstract
Semantic search tasks have grown extremely fast following the advancements in large language models, including the Reverse Dictionary and Word Sense Disambiguation in Arabic. This paper describes our participation in the Contemporary Arabic Dictionary Shared Task. We propose two models that achieved first place in both tasks. We conducted comprehensive experiments on the latest five multilingual sentence transformers and the Arabic BERT model for semantic embedding extraction. We achieved a ranking score of 0.06 for the reverse dictionary task, which is double than last year’s winner. We had an accuracy score of 0.268 for the Word Sense Disambiguation task.- Anthology ID:
- 2024.arabicnlp-1.77
- Volume:
- Proceedings of The Second Arabic Natural Language Processing Conference
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Nizar Habash, Houda Bouamor, Ramy Eskander, Nadi Tomeh, Ibrahim Abu Farha, Ahmed Abdelali, Samia Touileb, Injy Hamed, Yaser Onaizan, Bashar Alhafni, Wissam Antoun, Salam Khalifa, Hatem Haddad, Imed Zitouni, Badr AlKhamissi, Rawan Almatham, Khalil Mrini
- Venues:
- ArabicNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 697–703
- Language:
- URL:
- https://aclanthology.org/2024.arabicnlp-1.77
- DOI:
- 10.18653/v1/2024.arabicnlp-1.77
- Bibkey:
- Cite (ACL):
- Serry Sibaee, Abdullah Alharbi, Samar Ahmad, Omer Nacar, Anis Koubaa, and Lahouari Ghouti. 2024. ASOS at KSAA-CAD 2024: One Embedding is All You Need for Your Dictionary. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 697–703, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- ASOS at KSAA-CAD 2024: One Embedding is All You Need for Your Dictionary (Sibaee et al., ArabicNLP-WS 2024)
- Copy Citation:
- PDF:
- https://aclanthology.org/2024.arabicnlp-1.77.pdf
Export citation
@inproceedings{sibaee-etal-2024-asos-ksaa,
title = "{ASOS} at {KSAA}-{CAD} 2024: One Embedding is All You Need for Your Dictionary",
author = "Sibaee, Serry and
Alharbi, Abdullah and
Ahmad, Samar and
Nacar, Omer and
Koubaa, Anis and
Ghouti, Lahouari",
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-1.77",
doi = "10.18653/v1/2024.arabicnlp-1.77",
pages = "697--703",
abstract = "Semantic search tasks have grown extremely fast following the advancements in large language models, including the Reverse Dictionary and Word Sense Disambiguation in Arabic. This paper describes our participation in the Contemporary Arabic Dictionary Shared Task. We propose two models that achieved first place in both tasks. We conducted comprehensive experiments on the latest five multilingual sentence transformers and the Arabic BERT model for semantic embedding extraction. We achieved a ranking score of 0.06 for the reverse dictionary task, which is double than last year{'}s winner. We had an accuracy score of 0.268 for the Word Sense Disambiguation task.",
}
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%0 Conference Proceedings %T ASOS at KSAA-CAD 2024: One Embedding is All You Need for Your Dictionary %A Sibaee, Serry %A Alharbi, Abdullah %A Ahmad, Samar %A Nacar, Omer %A Koubaa, Anis %A Ghouti, Lahouari %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 sibaee-etal-2024-asos-ksaa %X Semantic search tasks have grown extremely fast following the advancements in large language models, including the Reverse Dictionary and Word Sense Disambiguation in Arabic. This paper describes our participation in the Contemporary Arabic Dictionary Shared Task. We propose two models that achieved first place in both tasks. We conducted comprehensive experiments on the latest five multilingual sentence transformers and the Arabic BERT model for semantic embedding extraction. We achieved a ranking score of 0.06 for the reverse dictionary task, which is double than last year’s winner. We had an accuracy score of 0.268 for the Word Sense Disambiguation task. %R 10.18653/v1/2024.arabicnlp-1.77 %U https://aclanthology.org/2024.arabicnlp-1.77 %U https://doi.org/10.18653/v1/2024.arabicnlp-1.77 %P 697-703
Markdown (Informal)
[ASOS at KSAA-CAD 2024: One Embedding is All You Need for Your Dictionary](https://aclanthology.org/2024.arabicnlp-1.77) (Sibaee et al., ArabicNLP-WS 2024)
- ASOS at KSAA-CAD 2024: One Embedding is All You Need for Your Dictionary (Sibaee et al., ArabicNLP-WS 2024)
ACL
- Serry Sibaee, Abdullah Alharbi, Samar Ahmad, Omer Nacar, Anis Koubaa, and Lahouari Ghouti. 2024. ASOS at KSAA-CAD 2024: One Embedding is All You Need for Your Dictionary. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 697–703, Bangkok, Thailand. Association for Computational Linguistics.