Automatic evaluating of Russian-Arabic machine translation quality using METEOR method

Hameed, Diadeen Ali; Faisal, Tahseen Ameen; Alshaykha, Ali Mustafa Ali; Hasan, Ghanim Thiab; Ali, Harith Abdullah

doi:10.1063/5.0067018

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…The system architecture (NMT) is shown in Figure 1. The algorithm used for performing English to Arabic translation can be explained with the help of the diagram shown [19].…”

Section: Methodsmentioning

confidence: 99%

DIA-English-Arabic neural machine translation domain: sulfur industry

Hameed

Faisal

Abbas

et al. 2022

IJEECS

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The aim of this paper is the design and development a new English-Arabic neural machine translation (NMT) called DIA translation system. The main purpose of the designing system is to study translator limited sulfur industry domain as a stand-alone tool in order to improve the translation quality. Machine translation (MT) are very sensitive to the domains they were trained on and can be integrated with general (English-Arabic) MT systems. The proposed system has mainly four directions: supports chemical symbols, terms, phrase, and text and it is evaluated by using (1,200) various English declarative sentences which written by English Language experts. The obtained results indicate that this system is high effective and has an accuracy of 79.33% in comparison with Google translator which has 38.67% for the same test samples.

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“…The system architecture (NMT) is shown in Figure 1. The algorithm used for performing English to Arabic translation can be explained with the help of the diagram shown [19].…”

Section: Methodsmentioning

confidence: 99%

DIA-English-Arabic neural machine translation domain: sulfur industry

Hameed

Faisal

Abbas

et al. 2022

IJEECS

Self Cite

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“…Similarly, the requirement forevaluating the MT arise [2]. Given the great development in the system of MT field, as well as the prominence of the requirement for great speed and extremely high degree of accuracy in the information convenience interchangeablely between two or more languages [3], human evaluation consumes more time in addition of being expensive and thus inappropriate to be used repeatedly during research or develop MT system engines [4].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of domain sulfur industry for DIA translator using bilingual evaluation understudy method

Mohammed Lateef,

Muter Awaad,

Ali Hameed

et al. 2024

Bulletin EEI

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Evaluation is important part of our system development cycle; it also contributes to improving new machine translation (MT) technology optimum via comparing them with the traditional systems available to determine the weaknesses and the effectiveness to be improved in the proposed MT system. This work aiming to make a study that evaluate the performance and effectivness of the domain sulfur industry (DSI) for English-Arabic DIA translator quality. The recent study has conducted evaluating by making a comparison between this programme with the prominent Google translator through applying a rendering of 1,200 English sentences in bilingual evaluation understudy (BLUE) method. The obtain results show that the efficiency of Google translator is about 30.325%, while DIA translator efficiency in domain sulfur industry is about 73.325% and it’s more effective and give a better translation accuracy. The BLUE method efficiency is about (90.478%) compared with the human expert evaluator.

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E-learning Visual Design Elements of User Experience Perspective

Alshaykha

2022

Tikrit j. eng. sci.

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The visual elements play an essential role in E-learning utilisations that impact the effectiveness of the learning users. That requires highlighting the effect of new elements of E-learning on the learning outcomes of students as well as users. The study was carried out in two phases. In the first phase, a literature review was conducted to identify the most relevant studies on the subject. This paper investigates empirical identification and examines both E-learning text and non-text related to the category of visual materials. The user experience design perspective covers literature surveys, interviews, and questionnaires. Research has been done on the types and functions of the visual elements of E-learning. Therefore, based on the existing E-learning Levin visual model elements, including "organisational", "descriptive", "interpretative", "deformable", "decorative", and "social", distinguish the correlation degree of each element with learning content and persistence. The result shows better user satisfaction enhancement and promotion of E-learning's learning effectiveness and persistence. In addition, from the perspective of user experience, it is found that social elements are the potential needs of users, and sociality is one of the characteristics of digital learning.

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Automatic evaluating of Russian-Arabic machine translation quality using METEOR method

Cited by 3 publications

References 13 publications

DIA-English-Arabic neural machine translation domain: sulfur industry

DIA-English-Arabic neural machine translation domain: sulfur industry

Evaluation of domain sulfur industry for DIA translator using bilingual evaluation understudy method

E-learning Visual Design Elements of User Experience Perspective

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