An efficient English to Hindi machine translation system using hybrid mechanism

Nair, Jayashree; Krishnan, Keerthana; Deetha, R

doi:10.1109/icacci.2016.7732363

Cited by 36 publications

(6 citation statements)

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“…Numerous studies in the area of machine translation have been conducted. For example, in the Hindi language, we found [11] based on declension principles, suggesting a machine translation mechanism for Hindi to English. They also discuss various machine translation techniques.…”

Section: Related Workmentioning

confidence: 88%

Automatic translation from English to Amazigh using transformer learning

Maarouf,

El Ayachi

et al. 2024

IJEECS

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Due to the lack of parallel data, to our knowledge, no study has been conducted on the Amazigh-English language pair, despite the numerous machine translation studies completed between major European language pairs. We decided to utilize the neural machine translation (NMT) method on a parallel corpus of 137,322 sentences. The attention-based encoder-decoder architecture is used to construct statistical machine translation (SMT) models based on Moses, as well as NMT models using long short-term memory (LSTM), gated recurrent units (GRU), and transformers. Various outcomes were obtained for each strategy after several simulations: 80.7% accuracy was achieved using the statistical approach, 85.2% with the GRU model, 87.9% with the LSTM model, and 91.37% with the transformer.

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Section: Related Workmentioning

confidence: 88%

Automatic translation from English to Amazigh using transformer learning

Maarouf,

El Ayachi

et al. 2024

IJEECS

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“…It is a recently proposed method utilizing special neural grid framework called Encoder-Decoder architecture [12]. It doesn't require any predefined features (features which are designed, not learned from the data).The goal of NMT is to build a model that maximizes the translation performance.…”

Section: Neural Machine Translation (Nmt)mentioning

confidence: 99%

A Survey on Hybrid Machine Translation

Anugu¹,

Ramesh²

2020

E3S Web Conf.

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Machine translation has gradually developed in past 1940’s.It has gained more and more attention because of effective and efficient nature. As it makes the translation automatically without the involvement of human efforts. The distinct models of machine translation along with “Neural Machine Translation (NMT)” is summarized in this paper. Researchers have previously done lots of work on Machine Translation techniques and their evaluation techniques. Thus, we want to demonstrate an analysis of the existing techniques for machine translation including Neural Machine translation, their differences and the translation tools associated with them. Now-a-days the combination of two Machine Translation systems has the full advantage of using features from both the systems which attracts in the domain of natural language processing. So, the paper also includes the literature survey of the Hybrid Machine Translation (HMT).

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“…Using this approach, plagiarized documents (in English) are created by automatically translating the source texts (in Urdu) using Google Translator (https://translate.google.com/, last visited: 20-02-2019). Note that Google Translator has been effectively used in earlier research studies [32,33].…”

Section: Plagiarized Documents a Plagiarized Document In Clpd-ue-19 mentioning

confidence: 99%

Design and Development of a Large Cross-Lingual Plagiarism Corpus for Urdu-English Language Pair

Haneef

Nawab

Munir

et al. 2019

Scientific Programming

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Cross-lingual plagiarism occurs when the source (or original) text(s) is in one language and the plagiarized text is in another language. In recent years, cross-lingual plagiarism detection has attracted the attention of the research community because a large amount of digital text is easily accessible in many languages through online digital repositories and machine translation systems are readily available, making it easier to perform cross-lingual plagiarism and harder to detect it. To develop and evaluate cross-lingual plagiarism detection systems, standard evaluation resources are needed. The majority of earlier studies have developed cross-lingual plagiarism corpora for English and other European language pairs. However, for Urdu-English language pair, the problem of cross-lingual plagiarism detection has not been thoroughly explored although a large amount of digital text is readily available in Urdu and it is spoken in many countries of the world (particularly in Pakistan, India, and Bangladesh). To fulfill this gap, this paper presents a large benchmark cross-lingual corpus for Urdu-English language pair. The proposed corpus contains 2,395 source-suspicious document pairs (540 are automatic translation, 539 are artificially paraphrased, 508 are manually paraphrased, and 808 are nonplagiarized). Furthermore, our proposed corpus contains three types of cross-lingual examples including artificial (automatic translation and artificially paraphrased), simulated (manually paraphrased), and real (nonplagiarized), which have not been previously reported in the development of cross-lingual corpora. Detailed analysis of our proposed corpus was carried out using n-gram overlap and longest common subsequence approaches. Using Word unigrams, mean similarity scores of 1.00, 0.68, 0.52, and 0.22 were obtained for automatic translation, artificially paraphrased, manually paraphrased, and nonplagiarized documents, respectively. These results show that documents in the proposed corpus are created using different obfuscation techniques, which makes the dataset more realistic and challenging. We believe that the corpus developed in this study will help to foster research in an underresourced language of Urdu and will be useful in the development, comparison, and evaluation of cross-lingual plagiarism detection systems for Urdu-English language pair. Our proposed corpus is free and publicly available for research purposes.

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An efficient English to Hindi machine translation system using hybrid mechanism

Cited by 36 publications

References 0 publications

Automatic translation from English to Amazigh using transformer learning

Automatic translation from English to Amazigh using transformer learning

A Survey on Hybrid Machine Translation

Design and Development of a Large Cross-Lingual Plagiarism Corpus for Urdu-English Language Pair

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