Machine Translation System Using Deep Learning for Punjabi to English

Deep, Kamal; Kumar, Ajit; Goyal, Vishal

doi:10.1007/978-981-15-7533-4_69

Cited by 6 publications

(3 citation statements)

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“…Several machine translation systems were built for English to Urdu, either using statistical machine translation approach, phrase-based approach, or rule-based approach; only a few have applied the neural machine translation approach. English to Punjabi machine translation system uses deep learning with a BLEU score of 34.38 for medium sentences [ 21 ]. Neural machine translation is a promising approach and has resulted in a good performance as compared to the statistical machine translation approach [ 21 ].…”

Section: Related Workmentioning

confidence: 99%

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Machine Translation System Using Deep Learning for English to Urdu

Andrabi

Wahid

2022

Computational Intelligence and Neuroscience

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Machine translation is an ongoing field of research from the last decades. The main aim of machine translation is to remove the language barrier. Earlier research in this field started with the direct word-to-word replacement of source language by the target language. Later on, with the advancement in computer and communication technology, there was a paradigm shift to data-driven models like statistical and neural machine translation approaches. In this paper, we have used a neural network-based deep learning technique for English to Urdu languages. Parallel corpus sizes of around 30923 sentences are used. The corpus contains sentences from English-Urdu parallel corpus, news, and sentences which are frequently used in day-to-day life. The corpus contains 542810 English tokens and 540924 Urdu tokens, and the proposed system is trained and tested using 70 : 30 criteria. In order to evaluate the efficiency of the proposed system, several automatic evaluation metrics are used, and the model output is also compared with the output from Google Translator. The proposed model has an average BLEU score of 45.83.

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Section: Related Workmentioning

confidence: 99%

“…English to Punjabi machine translation system uses deep learning with a BLEU score of 34.38 for medium sentences [ 21 ]. Neural machine translation is a promising approach and has resulted in a good performance as compared to the statistical machine translation approach [ 21 ].…”

Section: Related Workmentioning

confidence: 99%

Machine Translation System Using Deep Learning for English to Urdu

Andrabi

Wahid

2022

Computational Intelligence and Neuroscience

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“…That system consisted of a Japanese dictionary, a Japanese-Chinese dictionary, a Chinese dictionary, and a rule system, using a language implemented in a computer made in the United States. In the experiment, the system tried to translate 102 sentences that were used as experimental materials and 10 sentences that the author made at random within a certain range and got an accuracy rate of 90% [16]. For the Japanese-Chinese machine translation, the similarity was calculated using the method of concept classification and subsidiary language [17].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Model-Based Machine Learning for Chinese and Japanese Translation

Wang

2022

Wireless Communications and Mobile Computing

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This paper takes deep learning as the background of researcher design, combines the relevant cutting-edge research results in recent years, addresses the linguistic characteristics of Japanese and the problems faced by completing Japanese machine translation system, and determines the neural network structure of encoding-decoding for Japanese translation based on the characteristics of high similarity between Japanese and Chinese and after referring to the neural network architecture of English translation, and the basic structure and the corresponding improvement of the hidden layer unit calculation are carried out. The training model is optimized and an integrated Japanese machine translation system is implemented. Finally, the translation models of Japanese and Chinese intertranslation and Japanese and Chinese intertranslation are tested, and the optimal model fusion achieves a BLEU value of 39.52.

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