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            <title>Proceedings of the Fifth Arabic Natural Language Processing Workshop</title>
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            <namePart type="given">Muhammad</namePart>
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    <abstract>The difficulty of processing dialects is clearly observed in the high cost of building representative corpus, in particular for machine translation. Indeed, all machine translation systems require a huge amount and good management of training data, which represents a challenge in a low-resource setting such as the Tunisian Arabic dialect. In this paper, we present a data augmentation technique to create a parallel corpus for Tunisian Arabic dialect written in social media and standard Arabic in order to build a Machine Translation (MT) model. The created corpus was used to build a sentence-based translation model. This model reached a BLEU score of 15.03% on a test set, while it was limited to 13.27% utilizing the corpus without augmentation.</abstract>
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%0 Conference Proceedings
%T Parallel resources for Tunisian Arabic Dialect Translation
%A Kchaou, Saméh
%A Boujelbane, Rahma
%A Hadrich-Belguith, Lamia
%Y Zitouni, Imed
%Y Abdul-Mageed, Muhammad
%Y Bouamor, Houda
%Y Bougares, Fethi
%Y El-Haj, Mahmoud
%Y Tomeh, Nadi
%Y Zaghouani, Wajdi
%S Proceedings of the Fifth Arabic Natural Language Processing Workshop
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F kchaou-etal-2020-parallel
%X The difficulty of processing dialects is clearly observed in the high cost of building representative corpus, in particular for machine translation. Indeed, all machine translation systems require a huge amount and good management of training data, which represents a challenge in a low-resource setting such as the Tunisian Arabic dialect. In this paper, we present a data augmentation technique to create a parallel corpus for Tunisian Arabic dialect written in social media and standard Arabic in order to build a Machine Translation (MT) model. The created corpus was used to build a sentence-based translation model. This model reached a BLEU score of 15.03% on a test set, while it was limited to 13.27% utilizing the corpus without augmentation.
%U https://aclanthology.org/2020.wanlp-1.18
%P 200-206

Markdown (Informal)

[Parallel resources for Tunisian Arabic Dialect Translation](https://aclanthology.org/2020.wanlp-1.18) (Kchaou et al., WANLP 2020)

• Parallel resources for Tunisian Arabic Dialect Translation (Kchaou et al., WANLP 2020)

ACL

• Saméh Kchaou, Rahma Boujelbane, and Lamia Hadrich-Belguith. 2020. Parallel resources for Tunisian Arabic Dialect Translation. In Proceedings of the Fifth Arabic Natural Language Processing Workshop, pages 200–206, Barcelona, Spain (Online). Association for Computational Linguistics.