Aladdin-FTI @ AMIYA Three Wishes for Arabic NLP: Fidelity, Diglossia, and Multidialectal Generation
arXiv:2602.16290v1 Announce Type: new Abstract: Arabic dialects have long been under-represented in Natural Language Processing (NLP) research due to their non-standardization and high variability, which pose challenges for computational modeling. Recent advances in the field, such as Large Language Models (LLMs), offer promising avenues to address this gap by enabling Arabic to be modeled as a pluricentric language rather than a monolithic system. This paper presents Aladdin-FTI, our submission to the AMIYA shared task. The proposed system is designed to both generate and translate dialectal Arabic (DA). Specifically, the model supports text generation in Moroccan, Egyptian, Palestinian, Syrian, and Saudi dialects, as well as bidirectional translation between these dialects, Modern Standard Arabic (MSA), and English. The code and trained model are publicly available.
arXiv:2602.16290v1 Announce Type: new Abstract: Arabic dialects have long been under-represented in Natural Language Processing (NLP) research due to their non-standardization and high variability, which pose challenges for computational modeling. Recent advances in the field, such as Large Language Models (LLMs), offer promising avenues to address this gap by enabling Arabic to be modeled as a pluricentric language rather than a monolithic system. This paper presents Aladdin-FTI, our submission to the AMIYA shared task. The proposed system is designed to both generate and translate dialectal Arabic (DA). Specifically, the model supports text generation in Moroccan, Egyptian, Palestinian, Syrian, and Saudi dialects, as well as bidirectional translation between these dialects, Modern Standard Arabic (MSA), and English. The code and trained model are publicly available.
Executive Summary
This article presents Aladdin-FTI, a novel Arabic Natural Language Processing (NLP) system designed to address the under-representation of Arabic dialects in computational modeling. The proposed system supports generation and translation of dialectal Arabic (DA) in five dialects, as well as bidirectional translation between DA, Modern Standard Arabic (MSA), and English. The system leverages recent advances in Large Language Models (LLMs) to model Arabic as a pluricentric language. This development has significant implications for the representation of Arabic dialects in digital media, education, and language learning. The publicly available code and trained model enable researchers and developers to build upon this work, fostering further research and applications in Arabic NLP.
Key Points
- ▸ Aladdin-FTI is a novel Arabic NLP system designed to address the under-representation of Arabic dialects in computational modeling.
- ▸ The system supports generation and translation of dialectal Arabic (DA) in five dialects and bidirectional translation between DA, MSA, and English.
- ▸ The system leverages recent advances in Large Language Models (LLMs) to model Arabic as a pluricentric language.
Merits
Strength in Multidialectal Generation
The system's ability to generate text in multiple dialects of Arabic, including Moroccan, Egyptian, Palestinian, Syrian, and Saudi, is a significant strength. This capability enables the creation of more nuanced and context-specific language models, which can better capture the complexities of Arabic dialects.
Bidirectional Translation Capability
The system's bidirectional translation capability between dialectal Arabic, Modern Standard Arabic, and English is a noteworthy feature. This capability facilitates communication across language and dialectal boundaries, enabling more effective language exchange and understanding.
Public Availability of Code and Model
The publicly available code and trained model enable researchers and developers to build upon this work, fostering further research and applications in Arabic NLP. This openness promotes collaboration and accelerates the development of more sophisticated language models.
Demerits
Limited Evaluation Metrics
The article does not provide a comprehensive evaluation of the system's performance using standard metrics such as BLEU or METEOR. This limitation makes it difficult to assess the system's effectiveness and compare it to other Arabic NLP systems.
Lack of Contextualization
The article does not provide sufficient context about the specific dialects and languages being modeled, which may limit the system's applicability and generalizability to other dialects and languages.
Expert Commentary
The article presents a significant contribution to the field of Arabic NLP, addressing a long-standing challenge in computational modeling. The proposed system's ability to generate and translate dialectal Arabic in multiple dialects and languages is a notable achievement. However, the article's limitations, such as the lack of comprehensive evaluation metrics and contextualization, highlight the need for further research and development in this area. The publicly available code and model will undoubtedly facilitate further research and applications in Arabic NLP, but more work is needed to ensure that the system's capabilities are fully leveraged and its limitations addressed.
Recommendations
- ✓ Future research should prioritize the development of more comprehensive evaluation metrics to assess the system's performance and compare it to other Arabic NLP systems.
- ✓ Developers should consider incorporating more dialects and languages into the system to enhance its applicability and generalizability.
