scholarly journals Machine Translation System Using Deep Learning for English to Urdu

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Syed Abdul Basit Andrabi ◽  
Abdul Wahid
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Communication Technology ◽  
Target Language ◽  
Translation System ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Proposed Model ◽  
Learning Technique ◽  
Machine Translation System

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.

scholarly journals Research Chinese-Urdu Machine Translation Based on Deep Learning

2021 ◽  
Vol 3 (2) ◽  
pp. 34
Author(s):  
Zeshan Ali Ali
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
National Language ◽  
Translation System ◽  
Parallel Corpus ◽  
Electronic Dictionary ◽  
Sentence Level ◽  
Proposed Model ◽  
Machine Translation System ◽  
Transformer Model

Urdu is Pakistan 's national language. However, Chinese expertise is very negligible in Pakistan and the Asian nations. Yet fewer research has been undertaken in the area of computer translation on Chinese to Urdu. In order to solve the above problems, we designed of an electronic dictionary for Chinese-Urdu, and studied the sentence-level machine translation technology which is based on deep learning. The Design of an electronic dictionary Chinese-Urdu machine translation system we collected and constructed an electronic dictionary containing 24000 entries from Chinese to Urdu. For Sentence we used English as an intermediate language, and based on the existing parallel corpus of Chinese to English and English to Urdu, we constructed a bilingual parallel corpus containing 66000 sentences from Chinese to Urdu. The Corpus has trained by using two NMT Models (LSTM,Transformer Model) and the above two translation model were compared to the desired translation, with the help of bilingual valuation understudy (BLEU) score.  On NMT, The LSTM Model is gain of 0.067 to 0.41 in BLEU score while on Transformer model, there is gain of 0.077 to 0.52 in BLEU which is better than from LSTM Model score. Furthermore, we compared the proposed model with Google and Microsoft translation.

scholarly journals Factors Behind the Effectiveness of an Unsupervised Neural Machine Translation System between Korean and Japanese

2021 ◽  
Vol 11 (16) ◽  
pp. 7662
Author(s):  
Yong-Seok Choi ◽  
Yo-Han Park ◽  
Seung Yun ◽  
Sang-Hun Kim ◽  
Kong-Joo Lee
Keyword(s):  
Machine Translation ◽  
Word Order ◽  
Target Language ◽  
Translation System ◽  
Generation Model ◽  
Neural Machine Translation ◽  
Language Differences ◽  
Machine Translation System ◽  
The Common ◽  
Morphological System

Korean and Japanese have different writing scripts but share the same Subject-Object-Verb (SOV) word order. In this study, we pre-train a language-generation model using a Masked Sequence-to-Sequence pre-training (MASS) method on Korean and Japanese monolingual corpora. When building the pre-trained generation model, we allow the smallest number of shared vocabularies between the two languages. Then, we build an unsupervised Neural Machine Translation (NMT) system between Korean and Japanese based on the pre-trained generation model. Despite the different writing scripts and few shared vocabularies, the unsupervised NMT system performs well compared to other pairs of languages. Our interest is in the common characteristics of both languages that make the unsupervised NMT perform so well. In this study, we propose a new method to analyze cross-attentions between a source and target language to estimate the language differences from the perspective of machine translation. We calculate cross-attention measurements between Korean–Japanese and Korean–English pairs and compare their performances and characteristics. The Korean–Japanese pair has little difference in word order and a morphological system, and thus the unsupervised NMT between Korean and Japanese can be trained well even without parallel sentences and shared vocabularies.

scholarly journals Cadlaws – An English–French Parallel Corpus of Legally Equivalent Documents

2021 ◽  
Vol 14 (2) ◽  
pp. 494-508
Author(s):  
Francina Sole-Mauri ◽  
Pilar Sánchez-Gijón ◽  
Antoni Oliver
Keyword(s):  
Machine Translation ◽  
Translation System ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Legal Documents ◽  
Legal Traditions ◽  
Corpus Construction ◽  
Machine Translation System ◽  
French Corpus ◽  
Language Pair

This article presents Cadlaws, a new English–French corpus built from Canadian legal documents, and describes the corpus construction process and preliminary statistics obtained from it. The corpus contains over 16 million words in each language and includes unique features since it is composed of documents that are legally equivalent in both languages but not the result of a translation. The corpus is built upon enactments co-drafted by two jurists to ensure legal equality of each version and to re­flect the concepts, terms and institutions of two legal traditions. In this article the corpus definition as a parallel corpus instead of a comparable one is also discussed. Cadlaws has been pre-processed for machine translation and baseline Bilingual Evaluation Understudy (bleu), a score for comparing a candidate translation of text to a gold-standard translation of a neural machine translation system. To the best of our knowledge, this is the largest parallel corpus of texts which convey the same meaning in this language pair and is freely available for non-commercial use.

scholarly journals Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation

2017 ◽  
Vol 5 ◽  
pp. 339-351 ◽  
Author(s):  
Melvin Johnson ◽  
Mike Schuster ◽  
Quoc V. Le ◽  
Maxim Krikun ◽  
Yonghui Wu ◽  
...  
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
Target Language ◽  
Translation System ◽  
Single Model ◽  
Neural Machine Translation ◽  
Comparable Performance ◽  
Machine Translation System ◽  
Input Sentence ◽  
Multiple Languages

We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language. Using a shared wordpiece vocabulary, our approach enables Multilingual NMT systems using a single model. On the WMT’14 benchmarks, a single multilingual model achieves comparable performance for English→French and surpasses state-of-theart results for English→German. Similarly, a single multilingual model surpasses state-of-the-art results for French→English and German→English on WMT’14 and WMT’15 benchmarks, respectively. On production corpora, multilingual models of up to twelve language pairs allow for better translation of many individual pairs. Our models can also learn to perform implicit bridging between language pairs never seen explicitly during training, showing that transfer learning and zero-shot translation is possible for neural translation. Finally, we show analyses that hints at a universal interlingua representation in our models and also show some interesting examples when mixing languages.

scholarly journals Hindi Chhattisgarhi Machine Translation System Using Statistical Approach

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 02) ◽  
pp. 208-222
Author(s):  
Vikas Pandey ◽  
Dr.M.V. Padmavati ◽  
Dr. Ramesh Kumar
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Statistical Approach ◽  
Statistical Machine Translation ◽  
Target Language ◽  
Translation System ◽  
Parallel Corpus ◽  
Machine Translation System ◽  
Unknown Words ◽  
Language Pair

Machine Translation is a subfield of Natural language Processing (NLP) which uses to translate source language to target language. In this paper an attempt has been made to make a Hindi Chhattisgarhi machine translation system which is based on statistical approach. In the state of Chhattisgarh there is a long awaited need for Hindi to Chhattisgarhi machine translation system for converting Hindi into Chhattisgarhi especially for non Chhattisgarhi speaking people. In order to develop Hindi Chhattisgarhi statistical machine translation system an open source software called Moses is used. Moses is a statistical machine translation system and used to automatically train the translation model for Hindi Chhattisgarhi language pair called as parallel corpus. A collection of structured text to study linguistic properties is called corpus. This machine translation system works on parallel corpus of 40,000 Hindi-Chhattisgarhi bilingual sentences. In order to overcome translation problem related to proper noun and unknown words, a transliteration system is also embedded in it. These sentences are extracted from various domains like stories, novels, text books and news papers etc. This system is tested on 1000 sentences to check the grammatical correctness of sentences and it was found that an accuracy of 75% is achieved.

scholarly journals Development of Punjabi-English (PunEng) Parallel Corpus for Machine Translation System

2018 ◽  
Vol 7 (2) ◽  
pp. 690
Author(s):  
Kamal Deep ◽  
Ajit Kumar ◽  
Vishal Goyal
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Neural Machine Translation ◽  
Creation Process ◽  
Parallel Corpus ◽  
Main Requirement ◽  
Machine Translation System ◽  
The Creation

This paper describes the creation process and statistics of Punjabi English (PunEng) parallel corpus. Parallel corpus is the main requirement to develop statistical machine translation as well as neural machine translation. Until now, we do not have any availability of PunEng parallel corpus. In this paper, we have shown difficulties and intensive labor to develop parallel corpus. Methods used for collecting data and the results are discussed, errors during the process of collecting data and how to handle these errors will be described.

English-Dogri Translation System using MOSES

2016 ◽  
Vol 1 (1) ◽  
pp. 45-49
Author(s):  
Avinash Singh ◽  
Asmeet Kour ◽  
Shubhnandan S. Jamwal
Keyword(s):  
Natural Language Processing ◽  
Machine Translation ◽  
Language Processing ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Parallel Corpus ◽  
English System ◽  
Machine Translation System ◽  
Translation Machine ◽  
Language Pair

The objective behind this paper is to analyze the English-Dogri parallel corpus translation. Machine translation is the translation from one language into another language. Machine translation is the biggest application of the Natural Language Processing (NLP). Moses is statistical machine translation system allow to train translation models for any language pair. We have developed translation system using Statistical based approach which helps in translating English to Dogri and vice versa. The parallel corpus consists of 98,973 sentences. The system gives accuracy of 80% in translating English to Dogri and the system gives accuracy of 87% in translating Dogri to English system.

Analyzing Subword Techniques to Improve English to Sinhala Neural Machine Translation

2021 ◽  
pp. 2050017
Author(s):  
Rashmini Naranpanawa ◽  
Ravinga Perera ◽  
Thilakshi Fonseka ◽  
Uthayasanker Thayasivam
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Rare Word ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Low Resource ◽  
Word Level ◽  
Morphologically Rich Languages

Neural machine translation (NMT) is a remarkable approach which performs much better than the Statistical machine translation (SMT) models when there is an abundance of parallel corpus. However, vanilla NMT is primarily based upon word-level with a fixed vocabulary. Therefore, low resource morphologically rich languages such as Sinhala are mostly affected by the out of vocabulary (OOV) and Rare word problems. Recent advancements in subword techniques have opened up opportunities for low resource communities by enabling open vocabulary translation. In this paper, we extend our recently published state-of-the-art EN-SI translation system using the transformer and explore standard subword techniques on top of it to identify which subword approach has a greater effect on English Sinhala language pair. Our models demonstrate that subword segmentation strategies along with the state-of-the-art NMT can perform remarkably when translating English sentences into a rich morphology language regardless of a large parallel corpus.

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