dc.contributor.author |
Kumar, Joginder |
|
dc.contributor.author |
Rakhra, Manik |
|
dc.contributor.author |
Dubey, Preeti |
|
dc.date.accessioned |
2024-10-14T12:23:53Z |
|
dc.date.available |
2024-10-14T12:23:53Z |
|
dc.date.issued |
2024-10-14 |
|
dc.identifier.uri |
https://journal.uob.edu.bh:443/handle/123456789/5915 |
|
dc.description.abstract |
Machine translation has made significant progress in several Indian languages, but some,
known as computationally low-resourced languages, have seen very little work in this field. Dogri
language which is listed in the 8th Schedule of the Indian Constitution is one such language. The authors
has developed a Machine Translation System for the Hindi-Dogri pair in the fixed news domain using
three approaches: Rule-Based Machine Translation (developed using linguistic rules), Statistical
Machine Translation (built using the Moses toolkit), and Neural Machine Translation (developed using
neural networks). A comparison of all three approaches is presented in this paper. The paper also
discusses various research challenges identified in each approach used for machine translation. A corpus
of around 0.1 million sentences in the news domain was used for the development of corpus-based
techniques, i.e., SMT and NMT models. The authors also addressed the question of whether NMT
produces equivalent or better results compared to the SMT and RBMT approaches. Evaluation of the test
results was performed by language experts along with the Bilingual Evaluation Understudy (BLEU)
metric. In expert evaluation, it was observed that the NMT and SMT models' results are less ambiguous
compared to RBMT. The BLEU score of RBMTS is (79.65), SMT is (52.39) and Bidirectional
Embedding LSTM model of NMT is (52.46). The performances of the SMT and NMT models can
improve further with the increase in dataset (bilingual parallel corpus). |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
University Of Bahrain |
en_US |
dc.subject |
Machine Translation |
en_US |
dc.subject |
Hindi-Dogri Language Pair |
en_US |
dc.subject |
Low-Resourced Languages |
en_US |
dc.subject |
Neural Machine Translation (NMT) |
en_US |
dc.subject |
Statistical Machine Translation (SMT) |
en_US |
dc.subject |
Rule-Based Machine Translation (RBMT) |
en_US |
dc.title |
Comparative Analysis of Machine Translation for Hindi-Dogri Text Using Rule-Based, Statistical, and Neural Approaches |
en_US |
dc.type |
Article |
en_US |
dc.identifier.doi |
http://dx.doi.org/10.12785/ijcds/XXXXXX |
|
dc.volume |
17 |
en_US |
dc.issue |
1 |
en_US |
dc.pagestart |
1 |
en_US |
dc.pageend |
14 |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authoraffiliation |
School of Computer Applications, Lovely Professional University, Punjab |
en_US |
dc.contributor.authoraffiliation |
Department of Computer Science and Engineering, Lovely Professional University, Punjab |
en_US |
dc.contributor.authoraffiliation |
Department of Compute Science GCW, Parade, Jammu |
en_US |
dc.source.title |
International Journal of Computing and Digital Systems |
en_US |
dc.abbreviatedsourcetitle |
IJCDS |
en_US |