This aryRoBERTa
BASE model has been trained
from scratch on the Moroccan Arabic Wikipedia articles (
after removing the bot-generated articles), downloaded on the 1st of January 2023, processed using
Gensim Python library, preprocessed using
tr Linux/Unix utility and
CAMeLTools Python toolkit for Arabic NLP, and hosted here at
SaiedAlshahrani/Moroccan_Arabic_Wikipedia_20230101_nobots.
It achieves the following results on the evaluation set:
We trained this Moroccan Arabic Wikipedia Masked Language Model (aryRoBERTa
BASE) to evaluate its performance using the Fill-Mask evaluation task and the Masked Arab States Dataset (
MASD) dataset and measure the
impact of
bot-based generation on the Moroccan Arabic Wikipedia edition.
1@inproceedings{alshahrani-etal-2023-performance,
2 title = "{Performance Implications of Using Unrepresentative Corpora in {A}rabic Natural Language Processing}",
3 author = "Alshahrani, Saied and Alshahrani, Norah and Dey, Soumyabrata and Matthews, Jeanna",
4 booktitle = "Proceedings of the The First Arabic Natural Language Processing Conference (ArabicNLP 2023)",
5 month = December,
6 year = "2023",
7 address = "Singapore (Hybrid)",
8 publisher = "Association for Computational Linguistics",
9 url = "https://aclanthology.org/2023.arabicnlp-1.19",
10 doi = "10.18653/v1/2023.arabicnlp-1.19",
11 pages = "218--231",
12 abstract = "Wikipedia articles are a widely used source of training data for Natural Language Processing (NLP) research, particularly as corpora for low-resource languages like Arabic. However, it is essential to understand the extent to which these corpora reflect the representative contributions of native speakers, especially when many entries in a given language are directly translated from other languages or automatically generated through automated mechanisms. In this paper, we study the performance implications of using inorganic corpora that are not representative of native speakers and are generated through automated techniques such as bot generation or automated template-based translation. The case of the Arabic Wikipedia editions gives a unique case study of this since the Moroccan Arabic Wikipedia edition (ARY) is small but representative, the Egyptian Arabic Wikipedia edition (ARZ) is large but unrepresentative, and the Modern Standard Arabic Wikipedia edition (AR) is both large and more representative. We intrinsically evaluate the performance of two main NLP upstream tasks, namely word representation and language modeling, using word analogy evaluations and fill-mask evaluations using our two newly created datasets: Arab States Analogy Dataset (ASAD) and Masked Arab States Dataset (MASD). We demonstrate that for good NLP performance, we need both large and organic corpora; neither alone is sufficient. We show that producing large corpora through automated means can be a counter-productive, producing models that both perform worse and lack cultural richness and meaningful representation of the Arabic language and its native speakers.",
13}
We have trained this model on the Moroccan Arabic Wikipedia articles without bot-generated articles (
SaiedAlshahrani/Moroccan_Arabic_Wikipedia_20230101_nobots) without using any validation or evaluation data (only training data) due to a lack of computational power.
We have trained this model using the Paperspace GPU-Cloud service. We used a machine with 8 CPUs, 45GB RAM, and A6000 GPU with 48GB RAM.
This aryRoBERTa
BASE model has been evaluated on the Masked Arab States Dataset (
SaiedAlshahrani/MASD).
