Summary of Saudibert: a Large Language Model Pretrained on Saudi Dialect Corpora, by Faisal Qarah

SaudiBERT: A Large Language Model Pretrained on Saudi Dialect Corpora

by Faisal Qarah

First submitted to arxiv on: 10 May 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The paper introduces SaudiBERT, a monodialect Arabic language model pretrained exclusively on Saudi dialectal text. The authors compare SaudiBERT with six other multidialect Arabic language models across 11 evaluation datasets, divided into sentiment analysis and text classification tasks. SaudiBERT achieves state-of-the-art results in most tasks, outperforming the comparative models by a significant margin. Additionally, the paper presents two novel Saudi dialectal corpora: the Saudi Tweets Mega Corpus (STMC) and the Saudi Forums Corpus (SFC). These corpora are used to pretrain the proposed model and are the largest Saudi dialectal corpora reported in the literature. The results confirm the effectiveness of SaudiBERT in understanding and analyzing Arabic text expressed in Saudi dialect.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The paper is about a new language model called SaudiBERT that can understand and analyze Arabic text spoken in Saudi Arabia. This model is special because it was trained only on texts from Saudi Arabia, which makes it very good at understanding this type of language. The authors tested the model against other language models and found that it did much better. They also created two big collections of texts from Saudi Arabia, which they used to train the model. These texts are really helpful because there wasn’t anything like them before.