Statistical vs. Rule-Based Machine Translation: A Comparative Study on Indian Languages

Sreelekha, S; Bhattacharyya, Pushpak; Malathi, D.

doi:10.1007/978-981-10-5520-1_59

Cited by 15 publications

(7 citation statements)

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“…Results revealed that the RBMT system has reached performance comparable to publicly-available corpus-based MT systems between the languages. Sreelekha (2016) presented a case study between Statistical Machine Translation (SMT) and Rule-Based Machine Translation (RBMT) systems on English-Indian language and Indian to Indian language perspective. Results show that with a small amount of training corpus a statistical machine translation system has many advantages for high quality domain specific machine translation over rule-based systems.…”

Section: Introductionmentioning

confidence: 99%

A Rule-based Approach to English-Okun Prepositional Phrase Machine Translation

Esan,

Sobowale,

Adebiyi

et al. 2024

dujopas

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Okun culture is gradually going into extinction because the language is greatly dominated by English language. Therefore, this research developed a rule-based English-Okun machine translator using prepositional phrase. The dataset used were obtained from locally spoken words and the correctness of the re-write rules was tested using Java Formal Language Automata Package. Human judgment was employed in evaluating the system based on orthography accuracy. Results show that the developed translator is 95 percent accurate while experimental subject respondents have an accuracy of 58 percent. The developed machine translator’s performance is closer to the Experts’ but higher than that of the experimental subject respondents, this shows that indigenous people are not good at writing Okun language and find it hard to separate the dialect from Yoruba language. Hence, research in EnglishOkun machine translation should be intensified in order to prevent the language from going into extinction and future research should consider extending the full form bilingual lexicon of Okun language to achieve improved accuracy.

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Section: Introductionmentioning

confidence: 99%

A Rule-based Approach to English-Okun Prepositional Phrase Machine Translation

Esan,

Sobowale,

Adebiyi

et al. 2024

dujopas

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“…Many rules must be added to improve quality, resulting in a complex system [12]. Linguistic analysis was carried out on the input source sentences to extract information in terms of morphology, parts of speech, phrases, named entities, and word disambiguate [13], [14] . The second type is example-based machine translation (EBMT).…”

Section: Introductionmentioning

confidence: 99%

“…SMT assumes that the word from the target language is a translation of the source language word set with several possibilities [17], [18]. Decoding complexity and target language reordering are two significant concerns with SMT [19]. The last is neural machine translation (NMT), a fully automated neural network-based translation technology.…”

Section: Introductionmentioning

confidence: 99%

Neural Machine Translation of Spanish-English Food Recipes Using LSTM

Dedes

Utama

Wibawa

et al. 2022

JOIV : Int. J. Inform. Visualization

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Nowadays, food is one of the things that has been globalized, and everyone from different parts of the world has been able to cook food from other countries through existing online recipes. Based on that, this study developed a translation formula using a neural machine translation (NMT). NMT is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder–decoders. Our experiment led to novel insights and practical advice for building and extending NMT with the applied long short-term memory (LSTM) method to 47 bilingual food recipes between Spanish-English and English-Spanish. LSTM is one of the best machine learning methods for translating languages because it can retain memories for an extended period concurrently, grasp complicated connections between data, and provides highly useful information in deciding translation outcomes. The evaluation for this neural machine translation is to use BLEU. The comparing results show that the translation of recipes from Spanish-English has a better BLEU value of 0.998426 than English-Spanish with a data-sharing of 70%:30% during epoch 1000. Researchers can convert the country's popular cuisine recipes into another language for further research, allowing it to become more widely recognized abroad.

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“…In 1970s the Rule-Based Machine Translation (RBMT) also known as knowledge driven approach was developed to be the first method used in the area of MT which was based on the linguistics information [2]. The RBMT needs more human efforts since the preparation of the rules and linguistics resources involve morphological analyzers, partof-speech taggers, syntactic parsers, morphological generator and reordering the rules etc [3]. The RBMT was taken place by Statistical Machine Translation (SMT) method in the 1990s whose inspiration was based on the Bayes Theorem [4].…”

Section: Introductionmentioning

confidence: 99%

Machine Translation of English Language to Mizo Language

Thihlum

Khenglawt

Debnath

2020

2020 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)

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The communication between different regional languages is always inevitable fact which requires a lot of effort to make meaningful in terms of technology. The machine translation methods have been endeavoured to fill this impediment. The Neural Machine Translation is one of the efforts which recently gained a remarkable enhancement in the matter of human judgement over the conventional approaches like phrasebased machine translation and statistical machine translation. Thus, the advances of neural machine translation approach influence the challenge in the mind of people. The different types of online translation and mobile application tools such as Google Translate, DeepL, SYSTRAN, etc. applications were came up to tackle this issue. But such kinds of convenience translation tools are not available for English-Mizo parallel languages. In this paper, we have tried to enhance the ability of language translation by owing the supremacy capability of Neural Machine Translation to improve the English to Mizo conversion in BiLingual Evaluation Understudy metric.

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Statistical vs. Rule-Based Machine Translation: A Comparative Study on Indian Languages

Cited by 15 publications

References 1 publication

A Rule-based Approach to English-Okun Prepositional Phrase Machine Translation

A Rule-based Approach to English-Okun Prepositional Phrase Machine Translation

Neural Machine Translation of Spanish-English Food Recipes Using LSTM

Machine Translation of English Language to Mizo Language

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