TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation
Proceedings of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT5), 2022 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 wit...
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| creator | Nagoudi, El Moatez Billah Elmadany, AbdelRahim Abdul-Mageed, Muhammad |
| description | Proceedings of the 5th Workshop on Open-Source Arabic Corpora and
Processing Tools (OSACT5), 2022 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). |
| doi_str_mv | 10.48550/arxiv.2206.03933 |
| format | Article |
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Processing Tools (OSACT5), 2022 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).</description><identifier>DOI: 10.48550/arxiv.2206.03933</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Learning</subject><creationdate>2022-05</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2206.03933$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2206.03933$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Nagoudi, El Moatez Billah</creatorcontrib><creatorcontrib>Elmadany, AbdelRahim</creatorcontrib><creatorcontrib>Abdul-Mageed, Muhammad</creatorcontrib><title>TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation</title><description>Proceedings of the 5th Workshop on Open-Source Arabic Corpora and
Processing Tools (OSACT5), 2022 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)
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Processing Tools (OSACT5), 2022 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><doi>10.48550/arxiv.2206.03933</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Learning |
| title | TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation |
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