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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nasr-ben-hajhmida-2024-senit">
<titleInfo>
<title>SENIT at AraFinNLP2024: trust your model or combine two</title>
</titleInfo>
<name type="personal">
<namePart type="given">Abdelmomen</namePart>
<namePart type="family">Nasr</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Moez</namePart>
<namePart type="family">Ben HajHmida</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of The Second Arabic Natural Language Processing Conference</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nizar</namePart>
<namePart type="family">Habash</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Houda</namePart>
<namePart type="family">Bouamor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ramy</namePart>
<namePart type="family">Eskander</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nadi</namePart>
<namePart type="family">Tomeh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ibrahim</namePart>
<namePart type="family">Abu Farha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ahmed</namePart>
<namePart type="family">Abdelali</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Samia</namePart>
<namePart type="family">Touileb</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Injy</namePart>
<namePart type="family">Hamed</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yaser</namePart>
<namePart type="family">Onaizan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bashar</namePart>
<namePart type="family">Alhafni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wissam</namePart>
<namePart type="family">Antoun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Salam</namePart>
<namePart type="family">Khalifa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hatem</namePart>
<namePart type="family">Haddad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Imed</namePart>
<namePart type="family">Zitouni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Badr</namePart>
<namePart type="family">AlKhamissi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rawan</namePart>
<namePart type="family">Almatham</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Khalil</namePart>
<namePart type="family">Mrini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Bangkok, Thailand</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">nasr-ben-hajhmida-2024-senit</identifier>
<location>
<url>https://aclanthology.org/2024.arabicnlp-1.39</url>
</location>
<part>
<date>2024-08</date>
<extent unit="page">
<start>428</start>
<end>432</end>
</extent>
</part>
</mods>
</modsCollection>
%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.