A Review on Machine Translation Approaches

Kituku, Benson; Muchemi, Lawrence; Nganga, Wanjiku

doi:10.11591/ijeecs.v1.i1.pp182-190

Cited by 16 publications

(9 citation statements)

References 15 publications

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“…Based on the research findings on this study, literal translation technique is the most dominantly used technique that both Google Translate and U-dictionary apply in translating the English source text into the Indonesian target text. This result indicates that computation linguistic of Google Translate and U-dictionary is rule-based machine translation with direct translation approach (Kituku et al, 2016). In other words, both machines aresource textoriented.…”

Section: Discussionmentioning

confidence: 78%

“…Machine translation is a branch of computational linguistics, defined as automatic process through system. The issues of machine translation has been existence since 1940 and a lot of improvement has been built up (Kituku et al, 2016). Nowadays, linguistic computational plays a significant role in translation as machine translation has moved from text-based to speech-based.…”

Section: Machine Translationmentioning

confidence: 99%

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Comparison of Translation Techniques by Google Translate and U-Dictionary: How Differently Does Both Machine Translation Tools Perform in Translating?

Sipayung

Sianturi

Arta

et al. 2021

Elsya

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Better translation produced by computation linguistics should be evaluated through linguistics theory. This research aims to describe translation techniques between Google Translate and U-Dictionary. The study used a qualitative research method with a descriptive design. This design was used to describe the occurrences of translation techniques in both translation machine, with the researchers serving as an instrument to compare translation techniques which is produced on machine. The data are from expository text entitled “Importance of Good Manners in Every Day Life”. The total data are 122 words/phrases which are pairs of translations, English as source language and Indonesia as target language. The result shows that Google Translate apply five of Molina & Albir’s (2002) eighteen translation techniques, while U-dictionary apply seven techniques. Google Translate dominantly apply literal translation techniques (86,8%) followed by reduction translation techniques (4,9%). U-dictionary also dominantly apply literal translation techniques (75,4%), but follows with the variation translation techniques (13,1%). This study showed that both machines produced different target texts for the same source language due to different applications of techniques, with U-dictionary proven to apply more variety of translation techniques than Google Translate. The researcher hopes this study can be used as an evaluation for improving the performance of machine translations.

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Section: Discussionmentioning

confidence: 78%

Section: Machine Translationmentioning

confidence: 99%

Comparison of Translation Techniques by Google Translate and U-Dictionary: How Differently Does Both Machine Translation Tools Perform in Translating?

Sipayung

Sianturi

Arta

et al. 2021

Elsya

View full text Add to dashboard Cite

show abstract

“…Human evaluation of machine translation systems is highly subjective, time-consuming, and cannot be reused [4]. Recently, automatic evaluation methods have gained substantial attention due to their unprecedented benefits.…”

Section: Related Workmentioning

confidence: 99%

“…An increase in length penalty will lead to an increase in PD penalty. The proposed method also computes the common word's score by measuring the ratio between word overlap (i.e., Nc) between the reference and candidate sentence to the total number of unique words (i.e., Tw), as shown in (4).…”

Section: 𝑛𝑝𝑑_𝑠𝑐𝑜𝑟𝑒 = 𝑒𝑥𝑝|𝑃𝐷|mentioning

confidence: 99%

Semantics based English-Arabic machine translation evaluation

Beseiso

Tripathi²,

Al-Shboul

et al. 2022

IJEECS

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Some classic machine translation (MT) Evaluation methods, such as the bilingual evaluation understudy score (BLEU), have notably underperformed in evaluating machine translations for morphologically rich languages like Arabic. However, the recent remarkable advancements in the domain of word vectors and sentence vectors have opened up new research avenues for low-resource languages. This paper proposes a novel linguistic-based evaluation method for English-translated sentences in Arabic. The proposed approach includes penalties based on length, positions, and context-based schemes such as part-of-speech tagging (POS) and multilingual sentenceBERT (SBERT) models for machine translation evaluation. The proposed technique is tested using pearson correlation as a performance evaluation parameter and compared with state-of-the-art techniques. The experimental results demonstrate that the proposed model evidently outperforms other MT evaluation methods such as BLEU.

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“…It is the translation process from one natural language to another using machines or computers. MT is one of the most challenging applications in natural language processing, resulting from many difficulties, such as differences in grammar, morphology, and semantic analysis [1]. Arabic language and ArSL are natural languages for normal and deaf Arabic persons, respectively.…”

Section: Introductionmentioning

confidence: 99%

Automated Arabic-Arabic sign language translation system based on 3D avatar technology

Alethary

Aliwy

Ali

2022

IJAAS

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Arabic sign language (ArSL) is the natural language of the deaf community in Arabic countries. Deaf people have a set of difficulties due to poor services available. They have problems accessing essential information or receiving an education, communicating with other communities, and engaging in activities. Thus, a machine translation system of Arabic to ArSL has been developed using avatar technologies. Firstly, a dictionary of ArSL was constructed using eSign editor Software. The constructed dictionary has three thousand signs. It can be adopted for the translation system in which written text can be transformed into sign language. The dictionary will be available as a free resource for researchers. It is complex and time-consuming, but it is an essential step in the machine translation of whole Arabic text to ArSL with 3D animations. Secondly, the translator has been developed. It performs syntactic and morphological analysis and then applies a set of rules to translate an Arabic text into ArSL text based on the structure and grammar of ArSL. The system is evaluated according to the parallel corpus that consists of 180 sentences using the metric for evaluation of translation with explicit ordering metric for evaluation of translation with explicit ordering(METEOR) our system achieves a relative score of (86%).

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A Review on Machine Translation Approaches

Abstract: The

Cited by 16 publications

References 15 publications

Comparison of Translation Techniques by Google Translate and U-Dictionary: How Differently Does Both Machine Translation Tools Perform in Translating?

Comparison of Translation Techniques by Google Translate and U-Dictionary: How Differently Does Both Machine Translation Tools Perform in Translating?

Semantics based English-Arabic machine translation evaluation

Automated Arabic-Arabic sign language translation system based on 3D avatar technology

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