Abstract
We describe our submitted system to the 2024 Shared Task on The Arabic Financial NLP (Malaysha et al., 2024). We tackled Subtask 1, namely Multi-dialect Intent Detection. We used state-of-the-art pretrained contextualized text representation models and fine-tuned them according to the downstream task at hand. We started by finetuning multilingual BERT and various Arabic variants, namely MARBERTV1, MARBERTV2, and CAMeLBERT. Then, we employed an ensembling technique to improve our classification performance combining MARBERTV2 and CAMeLBERT embeddings. The findings indicate that MARBERTV2 surpassed all the other models mentioned.- Anthology ID:
- 2024.arabicnlp-1.39
- 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:
- 428–432
- Language:
- URL:
- https://aclanthology.org/2024.arabicnlp-1.39
- DOI:
- Bibkey:
- Cite (ACL):
- Abdelmomen Nasr and Moez Ben HajHmida. 2024. SENIT at AraFinNLP2024: trust your model or combine two. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 428–432, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- SENIT at AraFinNLP2024: trust your model or combine two (Nasr & Ben HajHmida, ArabicNLP-WS 2024)
- Copy Citation:
- PDF:
- https://aclanthology.org/2024.arabicnlp-1.39.pdf
Export citation
@inproceedings{nasr-ben-hajhmida-2024-senit, title = "{SENIT} at {A}ra{F}in{NLP}2024: trust your model or combine two", author = "Nasr, Abdelmomen and Ben HajHmida, Moez", 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-1.39", pages = "428--432", abstract = "We describe our submitted system to the 2024 Shared Task on The Arabic Financial NLP (Malaysha et al., 2024). We tackled Subtask 1, namely Multi-dialect Intent Detection. We used state-of-the-art pretrained contextualized text representation models and fine-tuned them according to the downstream task at hand. We started by finetuning multilingual BERT and various Arabic variants, namely MARBERTV1, MARBERTV2, and CAMeLBERT. Then, we employed an ensembling technique to improve our classification performance combining MARBERTV2 and CAMeLBERT embeddings. The findings indicate that MARBERTV2 surpassed all the other models mentioned.", }
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%0 Conference Proceedings %T SENIT at AraFinNLP2024: trust your model or combine two %A Nasr, Abdelmomen %A Ben HajHmida, Moez %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 nasr-ben-hajhmida-2024-senit %X We describe our submitted system to the 2024 Shared Task on The Arabic Financial NLP (Malaysha et al., 2024). We tackled Subtask 1, namely Multi-dialect Intent Detection. We used state-of-the-art pretrained contextualized text representation models and fine-tuned them according to the downstream task at hand. We started by finetuning multilingual BERT and various Arabic variants, namely MARBERTV1, MARBERTV2, and CAMeLBERT. Then, we employed an ensembling technique to improve our classification performance combining MARBERTV2 and CAMeLBERT embeddings. The findings indicate that MARBERTV2 surpassed all the other models mentioned. %U https://aclanthology.org/2024.arabicnlp-1.39 %P 428-432
Markdown (Informal)
[SENIT at AraFinNLP2024: trust your model or combine two](https://aclanthology.org/2024.arabicnlp-1.39) (Nasr & Ben HajHmida, ArabicNLP-WS 2024)
- SENIT at AraFinNLP2024: trust your model or combine two (Nasr & Ben HajHmida, ArabicNLP-WS 2024)
ACL
- Abdelmomen Nasr and Moez Ben HajHmida. 2024. SENIT at AraFinNLP2024: trust your model or combine two. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 428–432, Bangkok, Thailand. Association for Computational Linguistics.