English to Malayalam translation

Sebastian, Mary Priya; K, Sheena Kurian; Kumar, G. Santhosh

doi:10.1145/1858378.1858442

Cited by 13 publications

(3 citation statements)

References 6 publications

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“…The source language syntax was changed according to the syntax of the target language using some transformation rules and also using a morph analyzer for separating the suffixes from the root words in both languages. Sebastian et al [33] used parts of speech (POS) information along with bilingual corpus to improve the alignment model of an SMT system. Unnikrishnan et al [43] incorporated morphological and syntactic knowledge to the SMT system to improve the quality of translation.…”

Section: Related Workmentioning

confidence: 99%

Neural Machine Translation System for English to Indian Language Translation Using MTIL Parallel Corpus

Premjith

Kumar

2019

Journal of Intelligent Systems

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Introduction of deep neural networks to the machine translation research ameliorated conventional machine translation systems in multiple ways, specifically in terms of translation quality. The ability of deep neural networks to learn a sensible representation of words is one of the major reasons for this improvement. Despite machine translation using deep neural architecture is showing state-of-the-art results in translating European languages, we cannot directly apply these algorithms in Indian languages mainly because of two reasons: unavailability of the good corpus and Indian languages are morphologically rich. In this paper, we propose a neural machine translation (NMT) system for four language pairs: English–Malayalam, English–Hindi, English–Tamil, and English–Punjabi. We also collected sentences from different sources and cleaned them to make four parallel corpora for each of the language pairs, and then used them to model the translation system. The encoder network in the NMT architecture was designed with long short-term memory (LSTM) networks and bi-directional recurrent neural networks (Bi-RNN). Evaluation of the obtained models was performed both automatically and manually. For automatic evaluation, the bilingual evaluation understudy (BLEU) score was used, and for manual evaluation, three metrics such as adequacy, fluency, and overall ranking were used. Analysis of the results showed the presence of lengthy sentences in English–Malayalam, and the English–Hindi corpus affected the translation. Attention mechanism was employed with a view to addressing the problem of translating lengthy sentences (sentences contain more than 50 words), and the system was able to perceive long-term contexts in the sentences.

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Section: Related Workmentioning

confidence: 99%

Neural Machine Translation System for English to Indian Language Translation Using MTIL Parallel Corpus

Premjith

Kumar

2019

Journal of Intelligent Systems

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“…A rule based translation approach for English to Malayalam was proposed in [2]. In this method translation was mediated by English to Malayalam bilingual dictionaries and rules for converting source language structures into target language A methodology for translating English sentences to equivalent Malayalam using statistical model was proposed in [3]. The system used a monolingual Malayalam corpus and a bilingual English-Malayalam corpus in the training phase.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach to English to Malayalam Machine Translation

Nithya¹,

Joseph²

2013

IJCA

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Machine translation is the process of translating text from one natural language to other using computers. The process requires extreme intelligence and experience like a human being that a machine usually lacks. Availability of machine translators for translation from English to Dravidian language, Malayalam is on the low. A few corpus-based and non-corpus based approaches have been tried in performing English to Malayalam translation. In this work a hybrid approach to perform English to Malayalam translation is proposed. This hybrid approach extends the baseline statistical machine translator with a translation memory. A statistical machine translator performs translation by applying machine learning techniques on the corpus. The translation memory caches the recently performed translations in memory and eliminates the need for performing redundant translations. The system is implemented and evaluated using BLEU score and precision measure and the hybrid approach is found to improve the performance of the translator.

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“…The development of a Statistical Machine Translation (SMT) system for English to South Dravidian languages like Malayalam and Kannada by incorporating syntactic and morphological information was proposed by Unnikrishnan P, Antony P J and Dr. Soman K P [1]. A methodology for translating text from English to Malayalam using statistical models was proposed by Mary Priya Sebastian, Sheena Kurian K and G Santhosh Kumar [8]. This method incorporates morphological information into the parallel corpus with the help of a parts of speech tagger.…”

Section: Related Workmentioning

confidence: 99%

A hybrid English to Malayalam machine translator for Malayalam content creation in Wikis

Nithya¹

2013

IOSR-JCE

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English to Malayalam translation

Cited by 13 publications

References 6 publications

Neural Machine Translation System for English to Indian Language Translation Using MTIL Parallel Corpus

Neural Machine Translation System for English to Indian Language Translation Using MTIL Parallel Corpus

A Hybrid Approach to English to Malayalam Machine Translation

A hybrid English to Malayalam machine translator for Malayalam content creation in Wikis

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