  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).
