Evaluating Arabic to English Machine Translation

Hadla, Laith S.; Hailat, Taghreed M.; Al-Kabi, Mohammed N.

doi:10.14569/ijacsa.2014.051112

Cited by 10 publications

(7 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, the speed of translation provided by MT engines does not guarantee the quality of the output (Adly & Al Ansary, 2009;Al-mahasees, 2022;Hadla et al, 2014;Hijazi, 2013;Jabak, 2019). Hence, researchers in translation technology proposed two approaches to assess the quality of MT output; i.e., human and automatic machine translation quality assessment (Castillho, Doherty, Gaspari, & Moorkens;2018).…”

Section: Machine Translationmentioning

confidence: 99%

“…Several aspects of MT between Arabic and English have been investigated. For instance, some studies examined error typology, others conducted comparisons between several MT engines, and other studies analysed the progress of MT output through time (Adly & Al Ansary, 2009;Al-mahasees, 2022;Hadla, Hailat, & Al-Kabi, 2014;Hijazi, 2013;Jabak, 2019). However, Machine Translation Quality Assessment (MTQA) of legal texts from Arabic into English has not been studied before to the best knowledge of the researcher.…”

Section: Introductionmentioning

confidence: 99%

“…The error analysis showed that Google Translate output contained lexical and Google Translate Errors in Legal Texts: Machine Translation Alkatheery Lexical errors were the highest leading to faulty, inaccurate translations. The study concluded that the Google MT system cannot be used separately without any human posttranslation interference Hadla, Hailat, and Al-Kabi (2014). compared Google Translate and Babylon MT systems.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Google Translate Errors in Legal Texts: Machine Translation Quality Assessment

Alkatheery¹

2023

AWEJ‑tls

View full text Add to dashboard Cite

Machine translation received intensive research in different language pairs; yet, the quality of specialized translation; e.g., legal translation, from Arabic into English received little attention. The present study investigates the quality of machine translation of legal texts from Arabic into English. The paper aims at examining the errors found in the machine translation of legal texts from Arabic into English. It also studies the legal discourse features in machine-translation output. The research questions tackle the accuracy of machine translation of legal discourse and error categories and frequencies in machine translation. The researcher evaluated several factors to assess the quality of Google Translate; i.e., lexical, syntactic, and register-related errors. The study data consists of five legislative texts. The researcher conducted a manual error assessment and classification. To ensure the reliability of the error analysis, an existing human translation of the documents was used as a reference to ensure the reliability of the MT quality assessment and post-editing process. Later, the errors were classified, and their percentages and frequencies were calculated. A few examples of errors in each category were discussed and analysed. The highest error category was lexical errors scoring 43.4%. The last detected error category was deletion with a percentage of 1.7%. Syntactic errors constituted one-fourth of the errors found in the data. Legal register-related errors scored 30.2%. The subcategories of legal register-related errors varied in their occurrence; e.g., one-third of the errors related to legal discourse were legal terms. The study concluded that machine translation; though it provided a comprehensible output, could not translate legal structures and terminology perfectly.

show abstract

Section: Machine Translationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Google Translate Errors in Legal Texts: Machine Translation Quality Assessment

Alkatheery¹

2023

AWEJ‑tls

View full text Add to dashboard Cite

show abstract

“…MT can be classified into three main categories: into rule-based machine translation (RBMT), statistical machine translation (SMT), and NMT. RBMT relies on linguistic rules created by language experts, making it dependent on extensive dictionaries and significant linguistic knowledge [10]. However, building such resources can be expensive, and it is challenging to create rules that cover all languages.…”

Section: Introductionmentioning

confidence: 99%

From RNN techniques to pre-trained models: emphasis on the use in Arabic machine translation

Bensalah,

Ayad,

Adib

et al. 2024

IJ-AI

View full text Add to dashboard Cite

<p>Neural Machine Translation (NMT) has gained increasing attention in recent years due to its promising performance over conventional approaches such as Statistical Machine Translation. Nevertheless, when applied to languages with different structures, such as the pair (English, Arabic) that interests us in this work, its efficiency is degraded.<br />Alternatively, the use of unsupervised pre-training of large neural models has recently made a significant leap forward in the field of Natural Language Processing (NLP). By warm starting from published checkpoints, NLP practitioners have been able to push the state-of-the-art boundaries on multiple benchmarks while simultaneously saving considerable computational time. The emphasis so far has been mainly on natural language understanding problems. In this paper, we show the efficacy of pre-trained checkpoints for Arabic Machine Translation. We have evolved a transformer-based sequence-to-sequence model which is compatible with pre-trained checkpoints publicly available for Arabic Bidirectional Encoder Representations from Transformers (AraBERT) and Arabic Generative Pre-trained Transformer (AraGPT) and conducted a thorough empirical study on the usefulness of initializing our Arabic MT model, both encoder and decoder, with these checkpoints. Our models yield new state-of-the-art results in Arabic MT.</p>

show abstract

“…The proximity of the selected translation to the referred one is decided by a mutated n-gram accuracy when n={1, 2, 3, 4} [9]. The mutated n-gram accuracy is the essential standard that BLEU apply to differentiate among well done and weak selected translations [10], as this standard is centred on calculating the amount of highly occurred words in the selected translation as well as the referred rendering, followed by dividing the amount of the highly occurred words by the gross amount of words in the selected rendering [11]. The mutated n-gram accuracy determines selected linguistic structures as being shorter than those of referred opposite parts [12] in addition, this n-gram determines selected linguistic structures which have over generated correct word forms.…”

mentioning

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

View full text Add to dashboard Cite

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.

show abstract

Evaluating Arabic to English Machine Translation

Cited by 10 publications

References 19 publications

Google Translate Errors in Legal Texts: Machine Translation Quality Assessment

Google Translate Errors in Legal Texts: Machine Translation Quality Assessment

From RNN techniques to pre-trained models: emphasis on the use in Arabic machine translation

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

Contact Info

Product

Resources

About