This paper presents the Dialectal Arabic (DA) to Modern Standard Arabic (MSA) Machine Translation (MT) shared task in the sixth Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT6). The paper describes the creation of the validation and test data and the metrics used; and provides a brief overview of the submissions to the shared task. In all, 29 teams signed up and 6 teams made actual submissions. The teams used a variety of datasets and approaches to build their MT systems. The most successful submission involved using zero-shot and n-shot prompting of chatGPT.

This paper presents our approach to the Dialect to Modern Standard Arabic (MSA) Machine Translation shared task, conducted as part of the sixth Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT6). Our primary contribution is the development of a novel dataset derived from The Saudi Audio Dataset for Arabic (SADA) an Arabic audio corpus. By employing an automated method utilizing ChatGPT 3.5, we translated the dialectal Arabic texts to their MSA equivalents. This process not only yielded a unique and valuable dataset but also showcased an efficient method for leveraging language models in dataset generation. Utilizing this dataset, alongside additional resources, we trained a machine translation model based on the Transformer architecture. Through systematic experimentation with model configurations, we achieved notable improvements in translation quality. Our findings highlight the significance of LLM-assisted dataset creation methodologies and their impact on advancing machine translation systems, particularly for languages with considerable dialectal diversity like Arabic.

Arabic diacritic recovery i.e. diacritization is necessary for proper vocalization and an enabler for downstream applications such as language learning and text to speech. Diacritics come in two varieties, namely: core-word diacritics and case endings. In this paper we introduce a highly effective morphologically informed character-level model that can recover both types of diacritics simultaneously. The model uses a Recurrent Neural Network (RNN) based architecture that takes in text as a sequence of characters, with markers for morphological segmentation, and outputs a sequence of diacritics. We also introduce a character-based morphological segmentation model that we train for Modern Standard Arabic (MSA) and dialectal Arabic. We demonstrate the efficacy of our diacritization model on Classical Arabic, MSA, and two dialectal (Moroccan and Tunisian) texts. We achieve the lowest reported word-level diacritization error rate for MSA (3.4%), match the best results for Classical Arabic (5.4%), and report competitive results for dialectal Arabic.

Arabic diacritic recovery is important for a variety of downstream tasks such as text-to-speech. In this paper, we introduce a new Gulf Arabic diacritization dataset composed of 19,850 words based on a subset of the Gumar corpus. We provide comprehensive set of guidelines for diacritization to enable the diacritization of more data. We also report on diacritization results based on the new corpus using a Hidden Markov Model and character-based sequence to sequence models.