Automatic translation memory cleaning

Negri, Matteo; Ataman, Duygu; Sabet, Masoud Jalili; Turchi, Marco; Federico, Marcello

doi:10.1007/s10590-017-9191-5

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…This greatly improves the speed and efficiency of translation. The core technology of translation memory is to quickly find sentences in translation memory through deep learning technology and express their corresponding translations [ 24 ].…”

Section: Artificial Intelligence-based Machine English-assisted Trans...mentioning

confidence: 99%

Artificial Intelligence-based Machine English-Assisted Translation in the Internet of Things Environment

Zhang

Tang

2022

Computational Intelligence and Neuroscience

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With the development of Internet of Things technology, the things that machines do instead of humans are becoming more and more complicated. Machine translation has developed rapidly in the past few decades, and the translation system has also been greatly improved. People’s life and work are inseparable from machine translation, which brings a lot of convenience to people. But machine translation also has many flaws. Although machine translation can translate long texts in a very short time, its translation quality is quite poor, especially in the face of advanced English such as professional English, terminology, abbreviations, etc. To this end, machine English-assisted translation systems have been developed in recent years. Different from the working principle of machine English translation, machine English-assisted translation is a method of artificial intelligence + human-computer interaction. It uses convolutional neural networks and deep learning to translate words efficiently. The translator puts the original text and the translation into the machine database each time, and the machine can process some English terms, complex sentences, technical English, and other advanced English after continuous learning. Machine English-assisted translation can reduce repeated translations and greatly improve translation quality and translation efficiency. In this paper, the combination of artificial intelligence and machine English-assisted translation is compared with machine English translation, and comparative experiments are carried out by setting different matching degrees. Experiments show that the translation efficiency of machine English-assisted translation is much better than that of machine English translation. As the matching rate increases, the translation efficiency of machine English-assisted translation is higher. When the matching rate is greater than 80%, the translation efficiency is three times that of machine English translation. However, it is slightly insufficient in processing pure, simple statements. It highlights the advantages of machine English-assisted translation in terms of term translation and long complex sentences.

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Section: Artificial Intelligence-based Machine English-assisted Trans...mentioning

confidence: 99%

Artificial Intelligence-based Machine English-Assisted Translation in the Internet of Things Environment

Zhang

Tang

2022

Computational Intelligence and Neuroscience

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“…Machine Translator (MT), the system for translating languages. Today, research in this topics area are still done with a variety of additional new techniques to improve performance system [1][2] [3]. One process that is often used at the stage of pre-processing in NLP is stemming that aims to find the root of a word with eliminating all forms of affixes.…”

Section: Introductionmentioning

confidence: 99%

Multiprocessing Stemming: A Case Study of Indonesian Stemming

Yusliani¹,

Primartha²,

Marieska³

2019

IJCA

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Research in the field of Natural Language Processing (NLP) is currently increasing especially with the arrival of a new term that is "big data". The needs of the programming library that ready-touse becomes very important to speed up the phases of research. Some libraries that have already been mature is available but generally for English language and its dependently. So, it can't be used for other languages. Stemming is one of the basic processes that exist in NLP. Indonesian stemming algorithm that often used is ECS (Enhanced Confix-Stripping). One of the libraries that already implemented the algorithm is Sastrawi 1. Results from the experiment show that the time of stemming processing by Sastrawi is still slow. Therefore, this research will optimize the speed of stemming processing using multiprocessing (MP). The data test are used in this research has manually taken from Wikipedia 2. The experiment results show that the MP technique can decrease the average time of stemming processing about 98.45%.

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Design of English Automatic Translation System Based on Machine Intelligent Improved GLR Algorithm

Wang

2023

Lecture Notes on Data Engineering and Communications Technologies

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Automatic translation memory cleaning

Cited by 3 publications

References 14 publications

Artificial Intelligence-based Machine English-Assisted Translation in the Internet of Things Environment

Artificial Intelligence-based Machine English-Assisted Translation in the Internet of Things Environment

Multiprocessing Stemming: A Case Study of Indonesian Stemming

Design of English Automatic Translation System Based on Machine Intelligent Improved GLR Algorithm

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