Neural machine translation for Tamil to English

Jain, Minni; Punia, Ravneet; Hooda, Ishika

doi:10.1080/09720510.2020.1799582

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…The performance of NMT schemes even in the case of noisy data could be improved with Fuzzy match retrieval method combined with source-target concatenation. 42 In works, [43][44][45][46][47][48][49][50] CAT tools for translating into Indian languages were presented. In Reference 50, the authors presented an open source and extendable Morphological Analyser cum Generator (MAG) for Tamil language.…”

Section: Related Workmentioning

confidence: 99%

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Novel algorithm machine translation for language translation tool

Jayasakthi Velmurugan,

Sumathy,

Pradeep

2024

Computational Intelligence

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Fuzzy matching techniques are the presently used methods in translating the words. Neural machine translation and statistical machine translation are the methods used in MT. In machine translator tool, the strategy employed for translation needs to handle large amount of datasets and therefore the performance in retrieving correct matching output can be affected. In order to improve the matching score of MT, the advanced techniques can be presented by modifying the existing fuzzy based translator and neural machine translator. The conventional process of modifying architectures and encoding schemes are tedious process. Similarly, the preprocessing of datasets also involves more time consumption and memory utilization. In this article, a new spider web based searching enhanced translation is presented to be employed with the neural machine translator. The proposed scheme enables deep searching of available dataset to detect the accurate matching result. In addition, the quality of translation is improved by presenting an optimal selection scheme for using the sentence matches in source augmentation. The matches retrieved using various matching scores are applied to an optimization algorithm. The source augmentation using optimal retrieved matches increases the translation quality. Further, the selection of optimal match combination helps to reduce time requirement, since it is not necessary to test all retrieved matches in finding target sentence. The performance of translation is validated by measuring the quality of translation using BLEU and METEOR scores. These two scores can be achieved for the TA‐EN language pairs in different configurations of about 92% and 86%, correspondingly. The results are evaluated and compared with other available NMT methods to validate the work.

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Section: Related Workmentioning

confidence: 99%

“…In works, 43–50 CAT tools for translating into Indian languages were presented. In Reference 50, the authors presented an open source and extendable Morphological Analyser cum Generator (MAG) for Tamil language.…”

Section: Related Workmentioning

confidence: 99%

Novel algorithm machine translation for language translation tool

Jayasakthi Velmurugan,

Sumathy,

Pradeep

2024

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…In works [50][51][52][53][54][55][56][57], CAT tools for translating into Indian languages were presented. In [57], the authors presented an open source and extendable Morphological Analyser cum Generator (MAG) for Tamil language.…”

Section: Related Workmentioning

confidence: 99%

Deep Searching Based Optimized Machine Translation for Language Translation Tool

Velmurugan¹,

Sumathy²,

Pradeep

2022

Preprint

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Fuzzy matching techniques are the presently used methods in translating the words. Neural machine translation and statistical machine translation are the methods used in MT. In machine translator tool, the strategy employed for translation needs to handle large amount of datasets and therefore the performance in retrieving correct matching output can be affected. In order to improve the matching score of MT, the advanced techniques can be presented by modifying the existing fuzzy based translator and neural machine translator. The conventional process of modifying architectures and encoding schemes are tedious process. Similarly the preprocessing of datasets also involves more time consumption and memory utilization. In this paper, a new spider web based searching enhanced translation is presented to be employed with the neural machine translator. The proposed scheme enables deep searching of available dataset to detect the accurate matching result. In addition, the quality of translation is improved by presenting an optimal selection scheme for using the sentence matches in source augmentation. The matches retrieved using various matching scores are applied to an optimization algorithm. The source augmentation using optimal retrieved matches increases the translation quality. Further, the selection of optimal match combination helps to reduce time requirement, since it is not necessary to test all retrieved matches in finding target sentence. The performance of translation is validated by measuring the quality of translation using BLEU score. The results are evaluated and compared with other available NMT methods to validate the work.

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