Detecting and Integrating Multiword Expression into English-Arabic Statistical Machine Translation

Ebrahim, Sara; Hegazy, Doaa; Mostafa, Mostafa G. M.; El-Beltagy, Samhaa R.

doi:10.1016/j.procs.2017.10.099

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…For web translation, detecting a type of language has been developed also such as in [16] by detecting linguistic pattern. They used Arabic and English, while we used Bahasa Indonesia and English.…”

Section: Related Workmentioning

confidence: 99%

Quality Translation Enhancement Using Sequence Knowledge and Pruning in Statistical Machine Translation

2018

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Section: Related Workmentioning

confidence: 99%

Quality Translation Enhancement Using Sequence Knowledge and Pruning in Statistical Machine Translation

2018

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“…Therefore, it is necessary to analyze the long sentence in the translation of the long sentence [7]. In this context, this paper explores the various types of structural features of long sentences and their translation problems formed in translation practice based on natural language processing technology and comes up with a targeted method for translating long English sentences, which can help the author get rid of the constraints of the original text, so as to fully and effectively convey the information of the original text, obtain the functional equivalence between the source language and the translated into the original language, and produce the same reader's response [8].…”

Section: Introductionmentioning

confidence: 99%

“…Fengmei Shang, You Li. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] …”

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Exploring English Long Sentence Translation Methods by Applying Natural Language Processing Techniques

Shang,

2023

Applied Mathematics and Nonlinear Sciences

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This paper analyzes the “encoder-decoder” framework in neural machine translation and clarifies that the task of natural language processing is sequence learning. Secondly, recurrent neural networks are used to combine the historical hidden layer output information with the current input information, which is specialized in processing sequence data to achieve good translation results. Applying the attention mechanism to the field of natural language processing, a Transformer model based on the full attention mechanism is constructed in order to achieve the purpose of translating the source language while also performing alignment operations on the target language. The evaluation and analysis of the Transformer model based on the full-attention mechanism concludes that the Transformer model has 0.0152 Pearson correlation coefficients higher than the Bilingual Expert model, which is also 2.92% higher than the Bilingual Expert model, with the participation of f feature in both models. This further proves the Transformer model’s ability to correctly and effectively translate English sentences. At the same time, it also shows that the application of natural language processing technology can improve the efficiency of English long-sentence translation and comprehensively improve the quality of long-sentence translation.

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ParaDiom – A Parallel Corpus of Idiomatic Texts

Donaj,

Antloga

2023

Lecture Notes in Computer Science

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Detecting and Integrating Multiword Expression into English-Arabic Statistical Machine Translation

Cited by 4 publications

References 2 publications

Quality Translation Enhancement Using Sequence Knowledge and Pruning in Statistical Machine Translation

Quality Translation Enhancement Using Sequence Knowledge and Pruning in Statistical Machine Translation

Exploring English Long Sentence Translation Methods by Applying Natural Language Processing Techniques

ParaDiom – A Parallel Corpus of Idiomatic Texts

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