English to Punjabi statistical machine translation using moses (Corpus Based)

Jindal, Shishpal; Goyal, Vishal; Bhullar, Jaskarn Singh

doi:10.1080/09720510.2018.1471265

Cited by 9 publications

(5 citation statements)

References 2 publications

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“…Machine translation is a subfield of artificial intelligence that translates one natural language into another natural language with the help of computers [ 1 ]. It is an interdisciplinary field of research that incorporates ideas from different fields like languages, artificial intelligence, statistics, and mathematics [ 2 ]. The idea of machine translation can be traced back to the era when the computers came into existence.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Machine Translation System Using Deep Learning for English to Urdu

Andrabi

Wahid

2022

Computational Intelligence and Neuroscience

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“…Jindal et al 2018 used SMT based MT model for translation between English and Punjabi using three sets of parallel-sentence corpus achieving 0.8767 BLUE score [24].…”

Section: Recent Mt Research For Indian Languagesmentioning

confidence: 99%

A Survey on the MT Methods for Indian Languages: MT Challenges, Availability, and Production of Parallel Corpora, Government Policies and Research Directions

Sani,

Vijaya,

V Gangashetty

2024

IJCDS

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Since 1991, machine translation has been a prominent research area in India, with IIT Kanpur pioneering the original work which has since been expanded to several universities. Only 10 percent of India's 1.3 billion inhabitants can read, write, and speak English with varying degrees of competence, which makes machine translation crucial in overcoming the linguistic barrier to the internet. The Indian market for commercial products and events is greatly influenced by local languages, making the development and translation of region-based content an essential research topic nowadays. However, Indic-to-Indic language direct translation has faced several challenges and is still going through the experimental phase. Several government-sponsored projects are being undertaken in this regard. Still, there are limited sentence-aligned parallel bi-text resources available for the majority of Indian language pairs. This paper presents a detailed survey of the current trends of research on machine translation between Indian languages, along with their challenges over time. It also presents a timeline of recent research conducted and key findings of past surveys conducted over a decade. Under a single canopy, this paper provides sources of data, the progress made in developing datasets for low-resource Indian languages, various models of translation, encouragement from Indian Govt., and finally, new research directions.

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“…The raw text was obtained from different resources like Gyan Nidhi, EMILLE, Bible, Guru Granth Sahib electronic version, PSEB e-books, Bilingual newspapers, tourism and health websites. Also, Jindal et al (2018b) worked upon English to Punjabi machine translation using free translation software called Moses (Koehn et al, 2007). In their research, they created a corpus of 20000 sentences that were of different domains.…”

Section:  Parallel Seed Dictionarymentioning

confidence: 99%