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TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation

2022-05-27 18:05:50
El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed
arXiv_AI
   
Abstract

We present TURJUMAN, a neural toolkit for translating from 20 languages into Modern Standard Arabic (MSA). TURJUMAN exploits the recently-introduced text-to-text Transformer AraT5 model, endowing it with a powerful ability to decode into Arabic. The toolkit offers the possibility of employing a number of diverse decoding methods, making it suited for acquiring paraphrases for the MSA translations as an added value. To train TURJUMAN, we sample from publicly available parallel data employing a simple semantic similarity method to ensure data quality. This allows us to prepare and release AraOPUS-20, a new machine translation benchmark. We publicly release our translation toolkit (TURJUMAN) as well as our benchmark dataset (AraOPUS-20).

Abstract (translated)

URL

https://arxiv.org/abs/2206.03933

PDF

https://arxiv.org/pdf/2206.03933.pdf

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