TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation
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).
Cite
@article{arxiv.2206.03933,
title = {TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation},
author = {El Moatez Billah Nagoudi and AbdelRahim Elmadany and Muhammad Abdul-Mageed},
journal= {arXiv preprint arXiv:2206.03933},
year = {2022}
}
Comments
All authors contributed equally