Word-Order Issues in English-to-Urdu Statistical Machine Translation

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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“…Jawaid and Zeman proposed English to Urdu machine translation [ 14 ]. It is a system based on a statistical machine translation approach.…”

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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“…From a corpus of source-target sentence, we wrote many programs that perform different phases in order to extract only the Urdu syntax words. Firstly, for good predictive suggestion, collecting the words was a major challenge [18] and [4]. We designed little software for crawling, extracting, cleaning, counting and more to crawl the websites and then extracted only the Urdu syntax words, then cleaned all the other languages other than Urdu and then counted their occurrence on repeating to find out their frequency.…”

Section: Collecting Urdu Wordsmentioning

confidence: 99%

Effective Word Prediction in Urdu Language Using Stochastic Model

2018

SJCMS

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Word prediction and word suggestion is an important tools for writing contents in any language, in which the right word is predicted in a current context. Writing contents on English keyboards to produce other language contents is too hard and time consuming for everyone and requires more practice. To increase the typing speed especially in mobile and smart phones or for creating contents on social networks derived the need of this tool in every language. This paper presents a state of the art research for word prediction in Urdu Language (UL) based on stochastic model. Hidden Markov Model was implemented to predict the next state, while Unigram Model was also used to suggest the current state and the next hidden state, N-Gram Model was followed keeping N=2. The tool is developed to implement this model for Urdu Language (UL) and tested by regular and new URDU content writers to check their improvements in their typing speeds.

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“…Parallel corpus for statistical machine translation for English text into Urdu was presented by Aasim et al [8]. "Word Order Issues in English to Urdu Machine Translation" was presented by B. Jawaid and D. Zeman [9]. M. Zhang and H. Li investigated issues related to phrase reordering [20].…”

Section: Literature Reviewmentioning

confidence: 99%

“…We could not find any public domain machine translation tool developed specifically for Urdu. However some literature of basic MT techniques has been discovered [5][6][7][8][9]. In the current work a detailed survey is presented on the contemporary research in Urdu Machine translation (UMT).…”

Section: Introductionmentioning

confidence: 99%

Urdu to English Machine Translation using Bilingual Evaluation Understudy

Malik¹,

Habib²

2013

IJCA

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Machine Translation (MT) is exigent because it involves several thorny subtasks such as intrinsic language ambiguities, linguistic complexities and diversities between source and target language. Usually MT depends upon rules that provide linguistic information. At present, the corpus based MT approaches are used that include techniques like Example Based MT (EBMT) and Statistical MT (SMT). In addition to others, both of these corpus based techniques have different frameworks in the contemporary data-driven paradigm. SMT systems generate outputs using probabilities, whereas EBMT systems translate input text by matching examples from large amount of training data.Urdu MT is in its infancy with very limited availability of required data and computational resources. In this paper, we analyzed and evaluated the main MT techniques using qualitative as well as quantitative approaches. Strengths and weaknesses of each technique have been brought to light through special focus and discussion on examples from Urdu language MT literature.We evaluated the automated machine translated outputs using Bilingual Evaluation Understudy (BLEU). The EBMT approach produced the highest accuracy of 84.21% whereas the accuracy of the online SMT system is 62.68%. We found that BLUE scores of machine translated long Urdu sentences are low in comparison with long sentences. Similarly source text containing low frequency words affect the quality of Urdu machine translation negatively. Experiments and findings section of this paper explicate our reported results in detail. The paper concludes with proposal of future directions for research in Urdu machine translation.

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Word-Order Issues in English-to-Urdu Statistical Machine Translation

Cited by 19 publications

References 7 publications

Machine Translation System Using Deep Learning for English to Urdu

Machine Translation System Using Deep Learning for English to Urdu

Effective Word Prediction in Urdu Language Using Stochastic Model

Urdu to English Machine Translation using Bilingual Evaluation Understudy

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