On the Exploration of English to Urdu Machine Translation

Sadaf Abdul Rauf, Syeda Abida, Noor-e- Hira, Syeda Zahra, Dania Parvez, Javeria Bashir, Qurat-ul-ain Majid

Abstract
Machine Translation is the inevitable technology to reduce communication barriers in today’s world. It has made substantial progress in recent years and is being widely used in commercial as well as non-profit sectors. Such is only the case for European and other high resource languages. For English-Urdu language pair, the technology is in its infancy stage due to scarcity of resources. Present research is an important milestone in English-Urdu machine translation, as we present results for four major domains including Biomedical, Religious, Technological and General using Statistical and Neural Machine Translation. We performed series of experiments in attempts to optimize the performance of each system and also to study the impact of data sources on the systems. Finally, we established a comparison of the data sources and the effect of language model size on statistical machine translation performance.
Anthology ID:
2020.sltu-1.40
Volume:
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
SLTU
SIG:
Publisher:
European Language Resources association
Note:
Pages:
285–293
Language:
English
URL:
https://aclanthology.org/2020.sltu-1.40
DOI:
Bibkey:
Cite (ACL):
Sadaf Abdul Rauf, Syeda Abida, Noor-e- Hira, Syeda Zahra, Dania Parvez, Javeria Bashir, and Qurat-ul-ain Majid. 2020. On the Exploration of English to Urdu Machine Translation. In Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL), pages 285–293, Marseille, France. European Language Resources association.
Cite (Informal):
On the Exploration of English to Urdu Machine Translation (Abdul Rauf et al., SLTU 2020)
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https://preview.aclanthology.org/ingestion-script-update/2020.sltu-1.40.pdf
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